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Merge remote-tracking branch 'origin/develop' into feature/BAD 

Conflicts:
	.cproject
	gtsam/geometry/Cal3DS2.cpp
	gtsam/geometry/Cal3DS2.h
	gtsam/geometry/Cal3Unified.h
	gtsam/navigation/CombinedImuFactor.h
	gtsam/navigation/ImuFactor.h
	gtsam/nonlinear/NonlinearFactor.h
	gtsam/slam/tests/testPoseRotationPrior.cpp

Modified: testGaussMarkov1stOrderFactor.cpp, testPoseRotationPrior.cpp
release/4.3a0
dellaert 2014-11-04 17:04:57 +01:00
parent 7d57d91fec c212ba0984
commit a94835a2e4
64 changed files with 4318 additions and 2260 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

20
.cproject
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@ -848,18 +848,26 @@
<useDefaultCommand>true</useDefaultCommand>
<runAllBuilders>true</runAllBuilders>
</target>
<target name="testGaussianFactorGraph.run" path="build/gtsam/linear/tests" targetID="org.eclipse.cdt.build.MakeTargetBuilder">
<target name="testGaussMarkov1stOrderFactor.run" path="build/gtsam_unstable/slam/tests" targetID="org.eclipse.cdt.build.MakeTargetBuilder">
<buildCommand>make</buildCommand>
<buildArguments>-j5</buildArguments>
<buildTarget>testGaussianFactorGraph.run</buildTarget>
<buildTarget>testGaussMarkov1stOrderFactor.run</buildTarget>
<stopOnError>true</stopOnError>
<useDefaultCommand>true</useDefaultCommand>
<runAllBuilders>true</runAllBuilders>
</target>
<target name="testGaussianBayesNet.run" path="build/gtsam/linear/tests" targetID="org.eclipse.cdt.build.MakeTargetBuilder">
<target name="testGaussianFactorGraph.run" path="build/gtsam/linear/tests" targetID="org.eclipse.cdt.build.MakeTargetBuilder">
<buildCommand>make</buildCommand>
<buildArguments>-j5</buildArguments>
<buildTarget>testGaussianBayesNet.run</buildTarget>
<buildTarget>testGaussianFactorGraphUnordered.run</buildTarget>
<stopOnError>true</stopOnError>
<useDefaultCommand>true</useDefaultCommand>
<runAllBuilders>true</runAllBuilders>
</target>
<target name="testGaussianBayesNetUnordered.run" path="build/gtsam/linear/tests" targetID="org.eclipse.cdt.build.MakeTargetBuilder">
<buildCommand>make</buildCommand>
<buildArguments>-j5</buildArguments>
<buildTarget>testGaussianBayesNetUnordered.run</buildTarget>
<stopOnError>true</stopOnError>
<useDefaultCommand>true</useDefaultCommand>
<runAllBuilders>true</runAllBuilders>
@ -2830,10 +2838,10 @@
<useDefaultCommand>true</useDefaultCommand>
<runAllBuilders>true</runAllBuilders>
</target>
<target name="testSmartProjectionPoseFactor.run" path="build/gtsam/slam/tests" targetID="org.eclipse.cdt.build.MakeTargetBuilder">
<target name="testImplicitSchurFactor.run" path="build/gtsam/slam/tests" targetID="org.eclipse.cdt.build.MakeTargetBuilder">
<buildCommand>make</buildCommand>
<buildArguments>-j5</buildArguments>
<buildTarget>testSmartProjectionPoseFactor.run</buildTarget>
<buildTarget>testImplicitSchurFactor.run</buildTarget>
<stopOnError>true</stopOnError>
<useDefaultCommand>true</useDefaultCommand>
<runAllBuilders>true</runAllBuilders>

1
.gitignore vendored
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@ -4,3 +4,4 @@
*.DS_Store
/examples/Data/dubrovnik-3-7-pre-rewritten.txt
/examples/Data/pose2example-rewritten.txt
/examples/Data/pose3example-rewritten.txt

6
CMakeLists.txt
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@ -2,6 +2,12 @@
project(GTSAM CXX C)
cmake_minimum_required(VERSION 2.6)
# new feature to Cmake Version > 2.8.12
# Mac ONLY. Define Relative Path on Mac OS
if(NOT DEFINED CMAKE_MACOSX_RPATH)
set(CMAKE_MACOSX_RPATH 0)
endif()
# Set the version number for the library
set (GTSAM_VERSION_MAJOR 3)
set (GTSAM_VERSION_MINOR 1)

22
cmake/FindMKL.cmake
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@ -138,12 +138,15 @@ ELSE() # UNIX and macOS
${MKL_ROOT_DIR}/lib/
)
FIND_LIBRARY(MKL_GNUTHREAD_LIBRARY
mkl_gnu_thread
PATHS
${MKL_ROOT_DIR}/lib/${MKL_ARCH_DIR}
${MKL_ROOT_DIR}/lib/
)
# MKL on Mac OS doesn't ship with GNU thread versions, only Intel versions (see above)
IF(NOT APPLE)
FIND_LIBRARY(MKL_GNUTHREAD_LIBRARY
mkl_gnu_thread
PATHS
${MKL_ROOT_DIR}/lib/${MKL_ARCH_DIR}
${MKL_ROOT_DIR}/lib/
)
ENDIF()
# Intel Libraries
IF("${MKL_ARCH_DIR}" STREQUAL "32")
@ -227,7 +230,12 @@ ELSE() # UNIX and macOS
endforeach()
endforeach()
SET(MKL_LIBRARIES ${MKL_LP_GNUTHREAD_LIBRARIES})
IF(APPLE)
SET(MKL_LIBRARIES ${MKL_LP_INTELTHREAD_LIBRARIES})
ELSE()
SET(MKL_LIBRARIES ${MKL_LP_GNUTHREAD_LIBRARIES})
ENDIF()
MARK_AS_ADVANCED(MKL_CORE_LIBRARY MKL_LP_LIBRARY MKL_ILP_LIBRARY
MKL_SEQUENTIAL_LIBRARY MKL_INTELTHREAD_LIBRARY MKL_GNUTHREAD_LIBRARY)
ENDIF()

6
examples/Data/pose3example-rewritten.txt
View File

@ -1,11 +1,11 @@
VERTEX_SE3:QUAT 0 0 0 0 0 0 0 1
VERTEX_SE3:QUAT 1 1.00137 0.01539 0.004948 0.190253 0.283162 -0.392318 0.85423
VERTEX_SE3:QUAT 2 1.9935 0.023275 0.003793 0.351729 0.597838 -0.584174 -0.421446
VERTEX_SE3:QUAT 2 1.9935 0.023275 0.003793 -0.351729 -0.597838 0.584174 0.421446
VERTEX_SE3:QUAT 3 2.00429 1.02431 0.018047 0.331798 -0.200659 0.919323 0.067024
VERTEX_SE3:QUAT 4 0.999908 1.05507 0.020212 -0.035697 -0.46249 0.445933 0.765488
EDGE_SE3:QUAT 0 1 1.00137 0.01539 0.004948 0.190253 0.283162 -0.392318 0.85423 10000 0 0 0 0 0 10000 0 0 0 0 10000 0 0 0 10000 0 0 10000 0 10000
EDGE_SE3:QUAT 1 2 0.523923 0.776654 0.326659 -0.311512 -0.656877 0.678505 -0.105373 10000 0 0 0 0 0 10000 0 0 0 0 10000 0 0 0 10000 0 0 10000 0 10000
EDGE_SE3:QUAT 1 2 0.523923 0.776654 0.326659 0.311512 0.656877 -0.678505 0.105373 10000 0 0 0 0 0 10000 0 0 0 0 10000 0 0 0 10000 0 0 10000 0 10000
EDGE_SE3:QUAT 2 3 0.910927 0.055169 -0.411761 0.595795 -0.561677 0.079353 0.568551 10000 0 0 0 0 0 10000 0 0 0 0 10000 0 0 0 10000 0 0 10000 0 10000
EDGE_SE3:QUAT 3 4 0.775288 0.228798 -0.596923 -0.592076 0.30338 -0.513225 0.542222 10000 0 0 0 0 0 10000 0 0 0 0 10000 0 0 0 10000 0 0 10000 0 10000
EDGE_SE3:QUAT 3 4 0.775288 0.228798 -0.596923 -0.592077 0.30338 -0.513226 0.542221 10000 0 0 0 0 0 10000 0 0 0 0 10000 0 0 0 10000 0 0 10000 0 10000
EDGE_SE3:QUAT 1 4 -0.577841 0.628016 -0.543592 -0.12525 -0.534379 0.769122 0.327419 10000 0 0 0 0 0 10000 0 0 0 0 10000 0 0 0 10000 0 0 10000 0 10000
EDGE_SE3:QUAT 3 0 -0.623267 0.086928 0.773222 0.104639 0.627755 0.766795 0.083672 10000 0 0 0 0 0 10000 0 0 0 0 10000 0 0 0 10000 0 0 10000 0 10000

11
examples/Data/simpleGraph10gradIter.txt Normal file
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@ -0,0 +1,11 @@
VERTEX_SE3:QUAT 0 0.000000 0.000000 0.000000 0.0008187 0.0011723 0.0895466 0.9959816
VERTEX_SE3:QUAT 1 0.000000 -0.000000 0.000000 0.0010673 0.0015636 0.1606931 0.9870026
VERTEX_SE3:QUAT 2 -0.388822 0.632954 0.001223 0.0029920 0.0014066 0.0258235 0.9996610
VERTEX_SE3:QUAT 3 -1.143204 0.050638 0.006026 -0.0012800 -0.0002767 -0.2850291 0.9585180
VERTEX_SE3:QUAT 4 -0.512416 0.486441 0.005171 0.0002681 0.0023574 0.0171476 0.9998502
EDGE_SE3:QUAT 1 2 1.000000 2.000000 0.000000 0.0000000 0.0000000 0.7071068 0.7071068 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 100.000000 0.000000 100.000000
EDGE_SE3:QUAT 2 3 -0.000000 1.000000 0.000000 0.0000000 0.0000000 0.7071068 0.7071068 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 100.000000 0.000000 100.000000
EDGE_SE3:QUAT 3 4 1.000000 1.000000 0.000000 0.0000000 0.0000000 0.7071068 0.7071068 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 100.000000 0.000000 100.000000
EDGE_SE3:QUAT 3 1 0.000001 2.000000 0.000000 0.0000000 0.0000000 1.0000000 0.0000002 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 100.000000 0.000000 100.000000
EDGE_SE3:QUAT 1 4 -1.000000 1.000000 0.000000 0.0000000 0.0000000 -0.7071068 0.7071068 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 100.000000 0.000000 100.000000
EDGE_SE3:QUAT 0 1 0.000000 0.000000 0.000000 0.0000000 0.0000000 0.0000000 1.0000000 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 100.000000 0.000000 100.000000

54
examples/Pose2SLAMExample_g2o.cpp
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@ -26,36 +26,72 @@
using namespace std;
using namespace gtsam;
// HOWTO: ./Pose2SLAMExample_g2o inputFile outputFile (maxIterations) (tukey/huber)
int main(const int argc, const char *argv[]) {
// Read graph from file
string g2oFile;
if (argc < 2)
g2oFile = findExampleDataFile("noisyToyGraph.txt");
else
g2oFile = argv[1];
string kernelType = "none";
int maxIterations = 100; // default
string g2oFile = findExampleDataFile("noisyToyGraph.txt"); // default
// Parse user's inputs
if (argc > 1){
g2oFile = argv[1]; // input dataset filename
// outputFile = g2oFile = argv[2]; // done later
}
if (argc > 3){
maxIterations = atoi(argv[3]); // user can specify either tukey or huber
}
if (argc > 4){
kernelType = argv[4]; // user can specify either tukey or huber
}
// reading file and creating factor graph
NonlinearFactorGraph::shared_ptr graph;
Values::shared_ptr initial;
boost::tie(graph, initial) = readG2o(g2oFile);
bool is3D = false;
if(kernelType.compare("none") == 0){
boost::tie(graph, initial) = readG2o(g2oFile,is3D);
}
if(kernelType.compare("huber") == 0){
std::cout << "Using robust kernel: huber " << std::endl;
boost::tie(graph, initial) = readG2o(g2oFile,is3D, KernelFunctionTypeHUBER);
}
if(kernelType.compare("tukey") == 0){
std::cout << "Using robust kernel: tukey " << std::endl;
boost::tie(graph, initial) = readG2o(g2oFile,is3D, KernelFunctionTypeTUKEY);
}
// Add prior on the pose having index (key) = 0
NonlinearFactorGraph graphWithPrior = *graph;
noiseModel::Diagonal::shared_ptr priorModel = //
noiseModel::Diagonal::Variances((Vector(3) << 1e-6, 1e-6, 1e-8));
graphWithPrior.add(PriorFactor<Pose2>(0, Pose2(), priorModel));
std::cout << "Adding prior on pose 0 " << std::endl;
GaussNewtonParams params;
params.setVerbosity("TERMINATION");
if (argc > 3) {
params.maxIterations = maxIterations;
std::cout << "User required to perform maximum " << params.maxIterations << " iterations "<< std::endl;
}
std::cout << "Optimizing the factor graph" << std::endl;
GaussNewtonOptimizer optimizer(graphWithPrior, *initial);
GaussNewtonOptimizer optimizer(graphWithPrior, *initial, params);
Values result = optimizer.optimize();
std::cout << "Optimization complete" << std::endl;
std::cout << "initial error=" <<graph->error(*initial)<< std::endl;
std::cout << "final error=" <<graph->error(result)<< std::endl;
if (argc < 3) {
result.print("result");
} else {
const string outputFile = argv[2];
std::cout << "Writing results to file: " << outputFile << std::endl;
writeG2o(*graph, result, outputFile);
NonlinearFactorGraph::shared_ptr graphNoKernel;
Values::shared_ptr initial2;
boost::tie(graphNoKernel, initial2) = readG2o(g2oFile);
writeG2o(*graphNoKernel, result, outputFile);
std::cout << "done! " << std::endl;
}
return 0;

89
examples/Pose3SLAMExample_changeKeys.cpp Normal file
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@ -0,0 +1,89 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Pose3SLAMExample_initializePose3.cpp
* @brief A 3D Pose SLAM example that reads input from g2o, and initializes the Pose3 using InitializePose3
* Syntax for the script is ./Pose3SLAMExample_changeKeys input.g2o rewritted.g2o
* @date Aug 25, 2014
* @author Luca Carlone
*/
#include <gtsam/slam/dataset.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/PriorFactor.h>
#include <fstream>
using namespace std;
using namespace gtsam;
int main(const int argc, const char *argv[]) {
// Read graph from file
string g2oFile;
if (argc < 2)
g2oFile = findExampleDataFile("pose3example.txt");
else
g2oFile = argv[1];
NonlinearFactorGraph::shared_ptr graph;
Values::shared_ptr initial;
bool is3D = true;
boost::tie(graph, initial) = readG2o(g2oFile, is3D);
bool add = false;
Key firstKey = 8646911284551352320;
std::cout << "Using reference key: " << firstKey << std::endl;
if(add)
std::cout << "adding key " << std::endl;
else
std::cout << "subtracting key " << std::endl;
if (argc < 3) {
std::cout << "Please provide output file to write " << std::endl;
} else {
const string inputFileRewritten = argv[2];
std::cout << "Rewriting input to file: " << inputFileRewritten << std::endl;
// Additional: rewrite input with simplified keys 0,1,...
Values simpleInitial;
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, *initial) {
Key key;
if(add)
key = key_value.key + firstKey;
else
key = key_value.key - firstKey;
simpleInitial.insert(key, initial->at(key_value.key));
}
NonlinearFactorGraph simpleGraph;
BOOST_FOREACH(const boost::shared_ptr<NonlinearFactor>& factor, *graph) {
boost::shared_ptr<BetweenFactor<Pose3> > pose3Between =
boost::dynamic_pointer_cast<BetweenFactor<Pose3> >(factor);
if (pose3Between){
Key key1, key2;
if(add){
key1 = pose3Between->key1() + firstKey;
key2 = pose3Between->key2() + firstKey;
}else{
key1 = pose3Between->key1() - firstKey;
key2 = pose3Between->key2() - firstKey;
}
NonlinearFactor::shared_ptr simpleFactor(
new BetweenFactor<Pose3>(key1, key2, pose3Between->measured(), pose3Between->get_noiseModel()));
simpleGraph.add(simpleFactor);
}
}
writeG2o(simpleGraph, simpleInitial, inputFileRewritten);
}
return 0;
}

74
examples/Pose3SLAMExample_g2o.cpp Normal file
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@ -0,0 +1,74 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Pose3SLAMExample_initializePose3.cpp
* @brief A 3D Pose SLAM example that reads input from g2o, and initializes the Pose3 using InitializePose3
* Syntax for the script is ./Pose3SLAMExample_initializePose3 input.g2o output.g2o
* @date Aug 25, 2014
* @author Luca Carlone
*/
#include <gtsam/slam/dataset.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <fstream>
using namespace std;
using namespace gtsam;
int main(const int argc, const char *argv[]) {
// Read graph from file
string g2oFile;
if (argc < 2)
g2oFile = findExampleDataFile("pose3example.txt");
else
g2oFile = argv[1];
NonlinearFactorGraph::shared_ptr graph;
Values::shared_ptr initial;
bool is3D = true;
boost::tie(graph, initial) = readG2o(g2oFile, is3D);
// Add prior on the first key
NonlinearFactorGraph graphWithPrior = *graph;
noiseModel::Diagonal::shared_ptr priorModel = //
noiseModel::Diagonal::Variances((Vector(6) << 1e-6, 1e-6, 1e-6, 1e-4, 1e-4, 1e-4));
Key firstKey = 0;
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, *initial) {
std::cout << "Adding prior to g2o file " << std::endl;
firstKey = key_value.key;
graphWithPrior.add(PriorFactor<Pose3>(firstKey, Pose3(), priorModel));
break;
}
std::cout << "Optimizing the factor graph" << std::endl;
GaussNewtonParams params;
params.setVerbosity("TERMINATION"); // this will show info about stopping conditions
GaussNewtonOptimizer optimizer(graphWithPrior, *initial, params);
Values result = optimizer.optimize();
std::cout << "Optimization complete" << std::endl;
std::cout << "initial error=" <<graph->error(*initial)<< std::endl;
std::cout << "final error=" <<graph->error(result)<< std::endl;
if (argc < 3) {
result.print("result");
} else {
const string outputFile = argv[2];
std::cout << "Writing results to file: " << outputFile << std::endl;
writeG2o(*graph, result, outputFile);
std::cout << "done! " << std::endl;
}
return 0;
}

68
examples/Pose3SLAMExample_initializePose3Chordal.cpp Normal file
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@ -0,0 +1,68 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Pose3SLAMExample_initializePose3.cpp
* @brief A 3D Pose SLAM example that reads input from g2o, and initializes the Pose3 using InitializePose3
* Syntax for the script is ./Pose3SLAMExample_initializePose3 input.g2o output.g2o
* @date Aug 25, 2014
* @author Luca Carlone
*/
#include <gtsam/slam/InitializePose3.h>
#include <gtsam/slam/dataset.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/PriorFactor.h>
#include <fstream>
using namespace std;
using namespace gtsam;
int main(const int argc, const char *argv[]) {
// Read graph from file
string g2oFile;
if (argc < 2)
g2oFile = findExampleDataFile("pose3example.txt");
else
g2oFile = argv[1];
NonlinearFactorGraph::shared_ptr graph;
Values::shared_ptr initial;
bool is3D = true;
boost::tie(graph, initial) = readG2o(g2oFile, is3D);
// Add prior on the first key
NonlinearFactorGraph graphWithPrior = *graph;
noiseModel::Diagonal::shared_ptr priorModel = //
noiseModel::Diagonal::Variances((Vector(6) << 1e-6, 1e-6, 1e-6, 1e-4, 1e-4, 1e-4));
Key firstKey = 0;
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, *initial) {
std::cout << "Adding prior to g2o file " << std::endl;
firstKey = key_value.key;
graphWithPrior.add(PriorFactor<Pose3>(firstKey, Pose3(), priorModel));
break;
}
std::cout << "Initializing Pose3 - chordal relaxation" << std::endl;
Values initialization = InitializePose3::initialize(graphWithPrior);
std::cout << "done!" << std::endl;
if (argc < 3) {
initialization.print("initialization");
} else {
const string outputFile = argv[2];
std::cout << "Writing results to file: " << outputFile << std::endl;
writeG2o(*graph, initialization, outputFile);
std::cout << "done! " << std::endl;
}
return 0;
}

72
examples/Pose3SLAMExample_initializePose3Gradient.cpp Normal file
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@ -0,0 +1,72 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Pose3SLAMExample_initializePose3.cpp
* @brief A 3D Pose SLAM example that reads input from g2o, and initializes the Pose3 using InitializePose3
* Syntax for the script is ./Pose3SLAMExample_initializePose3 input.g2o output.g2o
* @date Aug 25, 2014
* @author Luca Carlone
*/
#include <gtsam/slam/InitializePose3.h>
#include <gtsam/slam/dataset.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/PriorFactor.h>
#include <fstream>
using namespace std;
using namespace gtsam;
int main(const int argc, const char *argv[]) {
// Read graph from file
string g2oFile;
if (argc < 2)
g2oFile = findExampleDataFile("pose3example.txt");
else
g2oFile = argv[1];
NonlinearFactorGraph::shared_ptr graph;
Values::shared_ptr initial;
bool is3D = true;
boost::tie(graph, initial) = readG2o(g2oFile, is3D);
// Add prior on the first key
NonlinearFactorGraph graphWithPrior = *graph;
noiseModel::Diagonal::shared_ptr priorModel = //
noiseModel::Diagonal::Variances((Vector(6) << 1e-6, 1e-6, 1e-6, 1e-4, 1e-4, 1e-4));
Key firstKey = 0;
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, *initial) {
std::cout << "Adding prior to g2o file " << std::endl;
firstKey = key_value.key;
graphWithPrior.add(PriorFactor<Pose3>(firstKey, Pose3(), priorModel));
break;
}
std::cout << "Initializing Pose3 - Riemannian gradient" << std::endl;
bool useGradient = true;
Values initialization = InitializePose3::initialize(graphWithPrior, *initial, useGradient);
std::cout << "done!" << std::endl;
std::cout << "initial error=" <<graph->error(*initial)<< std::endl;
std::cout << "initialization error=" <<graph->error(initialization)<< std::endl;
if (argc < 3) {
initialization.print("initialization");
} else {
const string outputFile = argv[2];
std::cout << "Writing results to file: " << outputFile << std::endl;
writeG2o(*graph, initialization, outputFile);
std::cout << "done! " << std::endl;
}
return 0;
}

4
gtsam/base/Value.h
View File

@ -36,9 +36,9 @@ namespace gtsam {
* Values can operate generically on Value objects, retracting or computing
* local coordinates for many Value objects of different types.
*
* Inhereting from the DerivedValue class templated provides a generic implementation of
* Inheriting from the DerivedValue class templated provides a generic implementation of
* the pure virtual functions retract_(), localCoordinates_(), and equals_(), eliminating
* the need to implement these functions in your class. Note that you must inheret from
* the need to implement these functions in your class. Note that you must inherit from
* DerivedValue templated on the class you are defining. For example you cannot define
* the following
* \code

146
gtsam/geometry/Cal3DS2.cpp
View File

@ -23,24 +23,9 @@
namespace gtsam {
/* ************************************************************************* */
Cal3DS2::Cal3DS2(const Vector &v):
fx_(v[0]), fy_(v[1]), s_(v[2]), u0_(v[3]), v0_(v[4]), k1_(v[5]), k2_(v[6]), p1_(v[7]), p2_(v[8]){}
/* ************************************************************************* */
Matrix Cal3DS2::K() const {
return (Matrix(3, 3) << fx_, s_, u0_, 0.0, fy_, v0_, 0.0, 0.0, 1.0);
}
/* ************************************************************************* */
Vector Cal3DS2::vector() const {
return (Vector(9) << fx_, fy_, s_, u0_, v0_, k1_, k2_, p1_, p2_);
}
/* ************************************************************************* */
void Cal3DS2::print(const std::string& s_) const {
gtsam::print(K(), s_ + ".K");
gtsam::print(Vector(k()), s_ + ".k");
Base::print(s_);
}
/* ************************************************************************* */
@ -52,135 +37,6 @@ bool Cal3DS2::equals(const Cal3DS2& K, double tol) const {
return true;
}
/* ************************************************************************* */
static Matrix29 D2dcalibration(double x, double y, double xx,
double yy, double xy, double rr, double r4, double pnx, double pny,
const Matrix2& DK) {
Matrix25 DR1;
DR1 << pnx, 0.0, pny, 1.0, 0.0, 0.0, pny, 0.0, 0.0, 1.0;
Matrix24 DR2;
DR2 << x * rr, x * r4, 2 * xy, rr + 2 * xx, //
y * rr, y * r4, rr + 2 * yy, 2 * xy;
Matrix29 D;
D << DR1, DK * DR2;
return D;
}
/* ************************************************************************* */
static Matrix2 D2dintrinsic(double x, double y, double rr,
double g, double k1, double k2, double p1, double p2,
const Matrix2& DK) {
const double drdx = 2. * x;
const double drdy = 2. * y;
const double dgdx = k1 * drdx + k2 * 2. * rr * drdx;
const double dgdy = k1 * drdy + k2 * 2. * rr * drdy;
// Dx = 2*p1*xy + p2*(rr+2*xx);
// Dy = 2*p2*xy + p1*(rr+2*yy);
const double dDxdx = 2. * p1 * y + p2 * (drdx + 4. * x);
const double dDxdy = 2. * p1 * x + p2 * drdy;
const double dDydx = 2. * p2 * y + p1 * drdx;
const double dDydy = 2. * p2 * x + p1 * (drdy + 4. * y);
Matrix2 DR;
DR << g + x * dgdx + dDxdx, x * dgdy + dDxdy, //
y * dgdx + dDydx, g + y * dgdy + dDydy;
return DK * DR;
}
/* ************************************************************************* */
Point2 Cal3DS2::uncalibrate(const Point2& p, boost::optional<Matrix&> H1,
boost::optional<Matrix&> H2) const {
// rr = x^2 + y^2;
// g = (1 + k(1)*rr + k(2)*rr^2);
// dp = [2*k(3)*x*y + k(4)*(rr + 2*x^2); 2*k(4)*x*y + k(3)*(rr + 2*y^2)];
// pi(:,i) = g * pn(:,i) + dp;
const double x = p.x(), y = p.y(), xy = x * y, xx = x * x, yy = y * y;
const double rr = xx + yy;
const double r4 = rr * rr;
const double g = 1. + k1_ * rr + k2_ * r4; // scaling factor
// tangential component
const double dx = 2. * p1_ * xy + p2_ * (rr + 2. * xx);
const double dy = 2. * p2_ * xy + p1_ * (rr + 2. * yy);
// Radial and tangential distortion applied
const double pnx = g * x + dx;
const double pny = g * y + dy;
Matrix2 DK;
if (H1 || H2) DK << fx_, s_, 0.0, fy_;
// Derivative for calibration
if (H1)
*H1 = D2dcalibration(x, y, xx, yy, xy, rr, r4, pnx, pny, DK);
// Derivative for points
if (H2)
*H2 = D2dintrinsic(x, y, rr, g, k1_, k2_, p1_, p2_, DK);
// Regular uncalibrate after distortion
return Point2(fx_ * pnx + s_ * pny + u0_, fy_ * pny + v0_);
}
/* ************************************************************************* */
Point2 Cal3DS2::calibrate(const Point2& pi, const double tol) const {
// Use the following fixed point iteration to invert the radial distortion.
// pn_{t+1} = (inv(K)*pi - dp(pn_{t})) / g(pn_{t})
const Point2 invKPi ((1 / fx_) * (pi.x() - u0_ - (s_ / fy_) * (pi.y() - v0_)),
(1 / fy_) * (pi.y() - v0_));
// initialize by ignoring the distortion at all, might be problematic for pixels around boundary
Point2 pn = invKPi;
// iterate until the uncalibrate is close to the actual pixel coordinate
const int maxIterations = 10;
int iteration;
for (iteration = 0; iteration < maxIterations; ++iteration) {
if (uncalibrate(pn).distance(pi) <= tol) break;
const double x = pn.x(), y = pn.y(), xy = x * y, xx = x * x, yy = y * y;
const double rr = xx + yy;
const double g = (1 + k1_ * rr + k2_ * rr * rr);
const double dx = 2 * p1_ * xy + p2_ * (rr + 2 * xx);
const double dy = 2 * p2_ * xy + p1_ * (rr + 2 * yy);
pn = (invKPi - Point2(dx, dy)) / g;
}
if ( iteration >= maxIterations )
throw std::runtime_error("Cal3DS2::calibrate fails to converge. need a better initialization");
return pn;
}
/* ************************************************************************* */
Matrix Cal3DS2::D2d_intrinsic(const Point2& p) const {
const double x = p.x(), y = p.y(), xx = x * x, yy = y * y;
const double rr = xx + yy;
const double r4 = rr * rr;
const double g = (1 + k1_ * rr + k2_ * r4);
Matrix2 DK;
DK << fx_, s_, 0.0, fy_;
return D2dintrinsic(x, y, rr, g, k1_, k2_, p1_, p2_, DK);
}
/* ************************************************************************* */
Matrix Cal3DS2::D2d_calibration(const Point2& p) const {
const double x = p.x(), y = p.y(), xx = x * x, yy = y * y, xy = x * y;
const double rr = xx + yy;
const double r4 = rr * rr;
const double g = (1 + k1_ * rr + k2_ * r4);
const double dx = 2 * p1_ * xy + p2_ * (rr + 2 * xx);
const double dy = 2 * p2_ * xy + p1_ * (rr + 2 * yy);
const double pnx = g * x + dx;
const double pny = g * y + dy;
Matrix2 DK;
DK << fx_, s_, 0.0, fy_;
return D2dcalibration(x, y, xx, yy, xy, rr, r4, pnx, pny, DK);
}
/* ************************************************************************* */
Cal3DS2 Cal3DS2::retract(const Vector& d) const {
return Cal3DS2(vector() + d);

86
gtsam/geometry/Cal3DS2.h
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@ -11,7 +11,7 @@
/**
* @file Cal3DS2.h
* @brief Calibration of a camera with radial distortion
* @brief Calibration of a camera with radial distortion, calculations in base class Cal3DS2_Base
* @date Feb 28, 2010
* @author ydjian
*/
@ -19,7 +19,7 @@
#pragma once
#include <gtsam/base/DerivedValue.h>
#include <gtsam/geometry/Point2.h>
#include <gtsam/geometry/Cal3DS2_Base.h>
namespace gtsam {
@ -37,29 +37,21 @@ namespace gtsam {
* k3 (rr + 2 Pn.y^2) + 2*k4 pn.x pn.y ]
* pi = K*pn
*/
class GTSAM_EXPORT Cal3DS2 {
class GTSAM_EXPORT Cal3DS2 : public Cal3DS2_Base, public DerivedValue<Cal3DS2> {
protected:
double fx_, fy_, s_, u0_, v0_ ; // focal length, skew and principal point
double k1_, k2_ ; // radial 2nd-order and 4th-order
double p1_, p2_ ; // tangential distortion
typedef Cal3DS2_Base Base;
public:
Matrix K() const ;
Eigen::Vector4d k() const { return Eigen::Vector4d(k1_, k2_, p1_, p2_); }
Vector vector() const ;
/// @name Standard Constructors
/// @{
/// Default Constructor with only unit focal length
Cal3DS2() : fx_(1), fy_(1), s_(0), u0_(0), v0_(0), k1_(0), k2_(0), p1_(0), p2_(0) {}
Cal3DS2() : Base() {}
Cal3DS2(double fx, double fy, double s, double u0, double v0,
double k1, double k2, double p1 = 0.0, double p2 = 0.0) :
fx_(fx), fy_(fy), s_(s), u0_(u0), v0_(v0), k1_(k1), k2_(k2), p1_(p1), p2_(p2) {}
Base(fx, fy, s, u0, v0, k1, k2, p1, p2) {}
virtual ~Cal3DS2() {}
@ -67,7 +59,7 @@ public:
/// @name Advanced Constructors
/// @{
Cal3DS2(const Vector &v) ;
Cal3DS2(const Vector &v) : Base(v) {}
/// @}
/// @name Testable
@ -79,57 +71,6 @@ public:
/// assert equality up to a tolerance
bool equals(const Cal3DS2& K, double tol = 10e-9) const;
/// @}
/// @name Standard Interface
/// @{
/// focal length x
inline double fx() const { return fx_;}
/// focal length x
inline double fy() const { return fy_;}
/// skew
inline double skew() const { return s_;}
/// image center in x
inline double px() const { return u0_;}
/// image center in y
inline double py() const { return v0_;}
/// First distortion coefficient
inline double k1() const { return k1_;}
/// Second distortion coefficient
inline double k2() const { return k2_;}
/// First tangential distortion coefficient
inline double p1() const { return p1_;}
/// Second tangential distortion coefficient
inline double p2() const { return p2_;}
/**
* convert intrinsic coordinates xy to (distorted) image coordinates uv
* @param p point in intrinsic coordinates
* @param Dcal optional 2*9 Jacobian wrpt Cal3DS2 parameters
* @param Dp optional 2*2 Jacobian wrpt intrinsic coordinates
* @return point in (distorted) image coordinates
*/
Point2 uncalibrate(const Point2& p,
boost::optional<Matrix&> Dcal = boost::none,
boost::optional<Matrix&> Dp = boost::none) const ;
/// Convert (distorted) image coordinates uv to intrinsic coordinates xy
Point2 calibrate(const Point2& p, const double tol=1e-5) const;
/// Derivative of uncalibrate wrpt intrinsic coordinates
Matrix D2d_intrinsic(const Point2& p) const ;
/// Derivative of uncalibrate wrpt the calibration parameters
Matrix D2d_calibration(const Point2& p) const ;
/// @}
/// @name Manifold
/// @{
@ -155,15 +96,10 @@ private:
template<class Archive>
void serialize(Archive & ar, const unsigned int version)
{
ar & BOOST_SERIALIZATION_NVP(fx_);
ar & BOOST_SERIALIZATION_NVP(fy_);
ar & BOOST_SERIALIZATION_NVP(s_);
ar & BOOST_SERIALIZATION_NVP(u0_);
ar & BOOST_SERIALIZATION_NVP(v0_);
ar & BOOST_SERIALIZATION_NVP(k1_);
ar & BOOST_SERIALIZATION_NVP(k2_);
ar & BOOST_SERIALIZATION_NVP(p1_);
ar & BOOST_SERIALIZATION_NVP(p2_);
ar & boost::serialization::make_nvp("Cal3DS2",
boost::serialization::base_object<Value>(*this));
ar & boost::serialization::make_nvp("Cal3DS2",
boost::serialization::base_object<Cal3DS2_Base>(*this));
}
};

187
gtsam/geometry/Cal3DS2_Base.cpp Normal file
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@ -0,0 +1,187 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Cal3DS2_Base.cpp
* @date Feb 28, 2010
* @author ydjian
*/
#include <gtsam/base/Vector.h>
#include <gtsam/base/Matrix.h>
#include <gtsam/geometry/Point2.h>
#include <gtsam/geometry/Point3.h>
#include <gtsam/geometry/Cal3DS2_Base.h>
namespace gtsam {
/* ************************************************************************* */
Cal3DS2_Base::Cal3DS2_Base(const Vector &v):
fx_(v[0]), fy_(v[1]), s_(v[2]), u0_(v[3]), v0_(v[4]), k1_(v[5]), k2_(v[6]), p1_(v[7]), p2_(v[8]){}
/* ************************************************************************* */
Matrix Cal3DS2_Base::K() const {
return (Matrix(3, 3) << fx_, s_, u0_, 0.0, fy_, v0_, 0.0, 0.0, 1.0);
}
/* ************************************************************************* */
Vector Cal3DS2_Base::vector() const {
return (Vector(9) << fx_, fy_, s_, u0_, v0_, k1_, k2_, p1_, p2_);
}
/* ************************************************************************* */
void Cal3DS2_Base::print(const std::string& s_) const {
gtsam::print(K(), s_ + ".K");
gtsam::print(Vector(k()), s_ + ".k");
}
/* ************************************************************************* */
bool Cal3DS2_Base::equals(const Cal3DS2_Base& K, double tol) const {
if (fabs(fx_ - K.fx_) > tol || fabs(fy_ - K.fy_) > tol || fabs(s_ - K.s_) > tol ||
fabs(u0_ - K.u0_) > tol || fabs(v0_ - K.v0_) > tol || fabs(k1_ - K.k1_) > tol ||
fabs(k2_ - K.k2_) > tol || fabs(p1_ - K.p1_) > tol || fabs(p2_ - K.p2_) > tol)
return false;
return true;
}
/* ************************************************************************* */
static Eigen::Matrix<double, 2, 9> D2dcalibration(double x, double y, double xx,
double yy, double xy, double rr, double r4, double pnx, double pny,
const Eigen::Matrix<double, 2, 2>& DK) {
Eigen::Matrix<double, 2, 5> DR1;
DR1 << pnx, 0.0, pny, 1.0, 0.0, 0.0, pny, 0.0, 0.0, 1.0;
Eigen::Matrix<double, 2, 4> DR2;
DR2 << x * rr, x * r4, 2 * xy, rr + 2 * xx, //
y * rr, y * r4, rr + 2 * yy, 2 * xy;
Eigen::Matrix<double, 2, 9> D;
D << DR1, DK * DR2;
return D;
}
/* ************************************************************************* */
static Eigen::Matrix<double, 2, 2> D2dintrinsic(double x, double y, double rr,
double g, double k1, double k2, double p1, double p2,
const Eigen::Matrix<double, 2, 2>& DK) {
const double drdx = 2. * x;
const double drdy = 2. * y;
const double dgdx = k1 * drdx + k2 * 2. * rr * drdx;
const double dgdy = k1 * drdy + k2 * 2. * rr * drdy;
// Dx = 2*p1*xy + p2*(rr+2*xx);
// Dy = 2*p2*xy + p1*(rr+2*yy);
const double dDxdx = 2. * p1 * y + p2 * (drdx + 4. * x);
const double dDxdy = 2. * p1 * x + p2 * drdy;
const double dDydx = 2. * p2 * y + p1 * drdx;
const double dDydy = 2. * p2 * x + p1 * (drdy + 4. * y);
Eigen::Matrix<double, 2, 2> DR;
DR << g + x * dgdx + dDxdx, x * dgdy + dDxdy, //
y * dgdx + dDydx, g + y * dgdy + dDydy;
return DK * DR;
}
/* ************************************************************************* */
Point2 Cal3DS2_Base::uncalibrate(const Point2& p, boost::optional<Matrix&> H1,
boost::optional<Matrix&> H2) const {
// rr = x^2 + y^2;
// g = (1 + k(1)*rr + k(2)*rr^2);
// dp = [2*k(3)*x*y + k(4)*(rr + 2*x^2); 2*k(4)*x*y + k(3)*(rr + 2*y^2)];
// pi(:,i) = g * pn(:,i) + dp;
const double x = p.x(), y = p.y(), xy = x * y, xx = x * x, yy = y * y;
const double rr = xx + yy;
const double r4 = rr * rr;
const double g = 1. + k1_ * rr + k2_ * r4; // scaling factor
// tangential component
const double dx = 2. * p1_ * xy + p2_ * (rr + 2. * xx);
const double dy = 2. * p2_ * xy + p1_ * (rr + 2. * yy);
// Radial and tangential distortion applied
const double pnx = g * x + dx;
const double pny = g * y + dy;
Eigen::Matrix<double, 2, 2> DK;
if (H1 || H2) DK << fx_, s_, 0.0, fy_;
// Derivative for calibration
if (H1)
*H1 = D2dcalibration(x, y, xx, yy, xy, rr, r4, pnx, pny, DK);
// Derivative for points
if (H2)
*H2 = D2dintrinsic(x, y, rr, g, k1_, k2_, p1_, p2_, DK);
// Regular uncalibrate after distortion
return Point2(fx_ * pnx + s_ * pny + u0_, fy_ * pny + v0_);
}
/* ************************************************************************* */
Point2 Cal3DS2_Base::calibrate(const Point2& pi, const double tol) const {
// Use the following fixed point iteration to invert the radial distortion.
// pn_{t+1} = (inv(K)*pi - dp(pn_{t})) / g(pn_{t})
const Point2 invKPi ((1 / fx_) * (pi.x() - u0_ - (s_ / fy_) * (pi.y() - v0_)),
(1 / fy_) * (pi.y() - v0_));
// initialize by ignoring the distortion at all, might be problematic for pixels around boundary
Point2 pn = invKPi;
// iterate until the uncalibrate is close to the actual pixel coordinate
const int maxIterations = 10;
int iteration;
for (iteration = 0; iteration < maxIterations; ++iteration) {
if (uncalibrate(pn).distance(pi) <= tol) break;
const double x = pn.x(), y = pn.y(), xy = x * y, xx = x * x, yy = y * y;
const double rr = xx + yy;
const double g = (1 + k1_ * rr + k2_ * rr * rr);
const double dx = 2 * p1_ * xy + p2_ * (rr + 2 * xx);
const double dy = 2 * p2_ * xy + p1_ * (rr + 2 * yy);
pn = (invKPi - Point2(dx, dy)) / g;
}
if ( iteration >= maxIterations )
throw std::runtime_error("Cal3DS2::calibrate fails to converge. need a better initialization");
return pn;
}
/* ************************************************************************* */
Matrix Cal3DS2_Base::D2d_intrinsic(const Point2& p) const {
const double x = p.x(), y = p.y(), xx = x * x, yy = y * y;
const double rr = xx + yy;
const double r4 = rr * rr;
const double g = (1 + k1_ * rr + k2_ * r4);
Eigen::Matrix<double, 2, 2> DK;
DK << fx_, s_, 0.0, fy_;
return D2dintrinsic(x, y, rr, g, k1_, k2_, p1_, p2_, DK);
}
/* ************************************************************************* */
Matrix Cal3DS2_Base::D2d_calibration(const Point2& p) const {
const double x = p.x(), y = p.y(), xx = x * x, yy = y * y, xy = x * y;
const double rr = xx + yy;
const double r4 = rr * rr;
const double g = (1 + k1_ * rr + k2_ * r4);
const double dx = 2 * p1_ * xy + p2_ * (rr + 2 * xx);
const double dy = 2 * p2_ * xy + p1_ * (rr + 2 * yy);
const double pnx = g * x + dx;
const double pny = g * y + dy;
Eigen::Matrix<double, 2, 2> DK;
DK << fx_, s_, 0.0, fy_;
return D2dcalibration(x, y, xx, yy, xy, rr, r4, pnx, pny, DK);
}
}
/* ************************************************************************* */

158
gtsam/geometry/Cal3DS2_Base.h Normal file
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@ -0,0 +1,158 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Cal3DS2.h
* @brief Calibration of a camera with radial distortion
* @date Feb 28, 2010
* @author ydjian
*/
#pragma once
#include <gtsam/base/DerivedValue.h>
#include <gtsam/geometry/Point2.h>
namespace gtsam {
/**
* @brief Calibration of a camera with radial distortion
* @addtogroup geometry
* \nosubgrouping
*
* Uses same distortionmodel as OpenCV, with
* http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html
* but using only k1,k2,p1, and p2 coefficients.
* K = [ fx s u0 ; 0 fy v0 ; 0 0 1 ]
* rr = Pn.x^2 + Pn.y^2
* \hat{pn} = (1 + k1*rr + k2*rr^2 ) pn + [ 2*k3 pn.x pn.y + k4 (rr + 2 Pn.x^2) ;
* k3 (rr + 2 Pn.y^2) + 2*k4 pn.x pn.y ]
* pi = K*pn
*/
class GTSAM_EXPORT Cal3DS2_Base {
protected:
double fx_, fy_, s_, u0_, v0_ ; // focal length, skew and principal point
double k1_, k2_ ; // radial 2nd-order and 4th-order
double p1_, p2_ ; // tangential distortion
public:
Matrix K() const ;
Eigen::Vector4d k() const { return Eigen::Vector4d(k1_, k2_, p1_, p2_); }
Vector vector() const ;
/// @name Standard Constructors
/// @{
/// Default Constructor with only unit focal length
Cal3DS2_Base() : fx_(1), fy_(1), s_(0), u0_(0), v0_(0), k1_(0), k2_(0), p1_(0), p2_(0) {}
Cal3DS2_Base(double fx, double fy, double s, double u0, double v0,
double k1, double k2, double p1 = 0.0, double p2 = 0.0) :
fx_(fx), fy_(fy), s_(s), u0_(u0), v0_(v0), k1_(k1), k2_(k2), p1_(p1), p2_(p2) {}
/// @}
/// @name Advanced Constructors
/// @{
Cal3DS2_Base(const Vector &v) ;
/// @}
/// @name Testable
/// @{
/// print with optional string
void print(const std::string& s = "") const ;
/// assert equality up to a tolerance
bool equals(const Cal3DS2_Base& K, double tol = 10e-9) const;
/// @}
/// @name Standard Interface
/// @{
/// focal length x
inline double fx() const { return fx_;}
/// focal length x
inline double fy() const { return fy_;}
/// skew
inline double skew() const { return s_;}
/// image center in x
inline double px() const { return u0_;}
/// image center in y
inline double py() const { return v0_;}
/// First distortion coefficient
inline double k1() const { return k1_;}
/// Second distortion coefficient
inline double k2() const { return k2_;}
/// First tangential distortion coefficient
inline double p1() const { return p1_;}
/// Second tangential distortion coefficient
inline double p2() const { return p2_;}
/**
* convert intrinsic coordinates xy to (distorted) image coordinates uv
* @param p point in intrinsic coordinates
* @param Dcal optional 2*9 Jacobian wrpt Cal3DS2 parameters
* @param Dp optional 2*2 Jacobian wrpt intrinsic coordinates
* @return point in (distorted) image coordinates
*/
Point2 uncalibrate(const Point2& p,
boost::optional<Matrix&> Dcal = boost::none,
boost::optional<Matrix&> Dp = boost::none) const ;
/// Convert (distorted) image coordinates uv to intrinsic coordinates xy
Point2 calibrate(const Point2& p, const double tol=1e-5) const;
/// Derivative of uncalibrate wrpt intrinsic coordinates
Matrix D2d_intrinsic(const Point2& p) const ;
/// Derivative of uncalibrate wrpt the calibration parameters
Matrix D2d_calibration(const Point2& p) const ;
private:
/// @}
/// @name Advanced Interface
/// @{
/** Serialization function */
friend class boost::serialization::access;
template<class Archive>
void serialize(Archive & ar, const unsigned int version)
{
ar & BOOST_SERIALIZATION_NVP(fx_);
ar & BOOST_SERIALIZATION_NVP(fy_);
ar & BOOST_SERIALIZATION_NVP(s_);
ar & BOOST_SERIALIZATION_NVP(u0_);
ar & BOOST_SERIALIZATION_NVP(v0_);
ar & BOOST_SERIALIZATION_NVP(k1_);
ar & BOOST_SERIALIZATION_NVP(k2_);
ar & BOOST_SERIALIZATION_NVP(p1_);
ar & BOOST_SERIALIZATION_NVP(p2_);
}
/// @}
};
}

18
gtsam/geometry/Cal3Unified.h
View File

@ -22,8 +22,8 @@
#pragma once
#include <gtsam/geometry/Cal3DS2.h>
#include <gtsam/geometry/Point2.h>
#include <gtsam/geometry/Cal3DS2_Base.h>
#include <gtsam/base/DerivedValue.h>
namespace gtsam {
@ -40,20 +40,18 @@ namespace gtsam {
* k3 (rr + 2 Pn.y^2) + 2*k4 pn.x pn.y ]
* pi = K*pn
*/
class GTSAM_EXPORT Cal3Unified : public Cal3DS2 {
class GTSAM_EXPORT Cal3Unified : public Cal3DS2_Base, public DerivedValue<Cal3Unified> {
typedef Cal3Unified This;
typedef Cal3DS2 Base;
typedef Cal3DS2_Base Base;
private:
double xi_; // mirror parameter
public:
/// dimension of the variable - used to autodetect sizes
static const size_t dimension = 10;
Vector vector() const ;
Vector vector() const ;
/// @name Standard Constructors
/// @{
@ -91,7 +89,7 @@ public:
/**
* convert intrinsic coordinates xy to image coordinates uv
* @param p point in intrinsic coordinates
* @param Dcal optional 2*9 Jacobian wrpt Cal3DS2 parameters
* @param Dcal optional 2*10 Jacobian wrpt Cal3Unified parameters
* @param Dp optional 2*2 Jacobian wrpt intrinsic coordinates
* @return point in image coordinates
*/
@ -131,6 +129,10 @@ private:
template<class Archive>
void serialize(Archive & ar, const unsigned int version)
{
ar & boost::serialization::make_nvp("Cal3Unified",
boost::serialization::base_object<Value>(*this));
ar & boost::serialization::make_nvp("Cal3Unified",
boost::serialization::base_object<Cal3DS2_Base>(*this));
ar & BOOST_SERIALIZATION_NVP(xi_);
}

21
gtsam/geometry/Unit3.cpp
View File

@ -21,7 +21,16 @@
#include <gtsam/geometry/Unit3.h>
#include <gtsam/geometry/Point2.h>
#include <boost/random/mersenne_twister.hpp>
#ifdef __clang__
# pragma clang diagnostic push
# pragma clang diagnostic ignored "-Wunused-variable"
#endif
#include <boost/random/uniform_on_sphere.hpp>
#ifdef __clang__
# pragma clang diagnostic pop
#endif
#include <boost/random/variate_generator.hpp>
#include <iostream>
@ -58,11 +67,11 @@ Unit3 Unit3::Random(boost::mt19937 & rng) {
}
/* ************************************************************************* */
const Matrix& Unit3::basis() const {
const Unit3::Matrix32& Unit3::basis() const {
// Return cached version if exists
if (B_.rows() == 3)
return B_;
if (B_)
return *B_;
// Get the axis of rotation with the minimum projected length of the point
Point3 axis;
@ -83,9 +92,9 @@ const Matrix& Unit3::basis() const {
b2 = b2 / b2.norm();
// Create the basis matrix
B_ = Matrix(3, 2);
B_ << b1.x(), b2.x(), b1.y(), b2.y(), b1.z(), b2.z();
return B_;
B_.reset(Unit3::Matrix32());
(*B_) << b1.x(), b2.x(), b1.y(), b2.y(), b1.z(), b2.z();
return *B_;
}
/* ************************************************************************* */

7
gtsam/geometry/Unit3.h
View File

@ -23,6 +23,7 @@
#include <gtsam/geometry/Point3.h>
#include <gtsam/base/DerivedValue.h>
#include <boost/random/mersenne_twister.hpp>
#include <boost/optional.hpp>
namespace gtsam {
@ -31,8 +32,10 @@ class GTSAM_EXPORT Unit3{
private:
typedef Eigen::Matrix<double,3,2> Matrix32;
Point3 p_; ///< The location of the point on the unit sphere
mutable Matrix B_; ///< Cached basis
mutable boost::optional<Matrix32> B_; ///< Cached basis
public:
@ -84,7 +87,7 @@ public:
* It is a 3*2 matrix [b1 b2] composed of two orthogonal directions
* tangent to the sphere at the current direction.
*/
const Matrix& basis() const;
const Matrix32& basis() const;
/// Return skew-symmetric associated with 3D point on unit sphere
Matrix skew() const;

16
gtsam/geometry/tests/testCal3Unified.cpp
View File

@ -19,6 +19,9 @@
#include <gtsam/base/numericalDerivative.h>
#include <gtsam/geometry/Cal3Unified.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/inference/Key.h>
using namespace gtsam;
GTSAM_CONCEPT_TESTABLE_INST(Cal3Unified)
@ -97,6 +100,19 @@ TEST( Cal3Unified, retract)
CHECK(assert_equal(d,K.localCoordinates(actual),1e-9));
}
/* ************************************************************************* */
TEST( Cal3Unified, DerivedValue)
{
Values values;
Cal3Unified cal(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
Key key = 1;
values.insert(key, cal);
Cal3Unified calafter = values.at<Cal3Unified>(key);
CHECK(assert_equal(cal,calafter,1e-9));
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
/* ************************************************************************* */

43
gtsam/geometry/tests/testPinholeCamera.cpp
View File

@ -183,25 +183,30 @@ TEST( PinholeCamera, Dproject)
}
/* ************************************************************************* */
//static Point2 projectInfinity3(const Pose3& pose, const Point2& point2D, const Cal3_S2& cal) {
// Point3 point(point2D.x(), point2D.y(), 1.0);
// return Camera(pose,cal).projectPointAtInfinity(point);
//}
//
//TEST( PinholeCamera, Dproject_Infinity)
//{
// Matrix Dpose, Dpoint, Dcal;
// Point2 point2D(-0.08,-0.08);
// Point3 point3D(point1.x(), point1.y(), 1.0);
// Point2 result = camera.projectPointAtInfinity(point3D, Dpose, Dpoint, Dcal);
// Matrix numerical_pose = numericalDerivative31(projectInfinity3, pose1, point2D, K);
// Matrix numerical_point = numericalDerivative32(projectInfinity3, pose1, point2D, K);
// Matrix numerical_cal = numericalDerivative33(projectInfinity3, pose1, point2D, K);
// CHECK(assert_equal(numerical_pose, Dpose, 1e-7));
// CHECK(assert_equal(numerical_point, Dpoint, 1e-7));
// CHECK(assert_equal(numerical_cal, Dcal, 1e-7));
//}
//
static Point2 projectInfinity3(const Pose3& pose, const Point3& point3D, const Cal3_S2& cal) {
return Camera(pose,cal).projectPointAtInfinity(point3D);
}
TEST( PinholeCamera, Dproject_Infinity)
{
Matrix Dpose, Dpoint, Dcal;
Point3 point3D(point1.x(), point1.y(), -10.0); // a point in front of the camera
// test Projection
Point2 actual = camera.projectPointAtInfinity(point3D, Dpose, Dpoint, Dcal);
Point2 expected(-5.0, 5.0);
CHECK(assert_equal(actual, expected, 1e-7));
// test Jacobians
Matrix numerical_pose = numericalDerivative31(projectInfinity3, pose1, point3D, K);
Matrix numerical_point = numericalDerivative32(projectInfinity3, pose1, point3D, K);
Matrix numerical_point2x2 = numerical_point.block(0,0,2,2); // only the direction to the point matters
Matrix numerical_cal = numericalDerivative33(projectInfinity3, pose1, point3D, K);
CHECK(assert_equal(numerical_pose, Dpose, 1e-7));
CHECK(assert_equal(numerical_point2x2, Dpoint, 1e-7));
CHECK(assert_equal(numerical_cal, Dcal, 1e-7));
}
/* ************************************************************************* */
static Point2 project4(const Camera& camera, const Point3& point) {
return camera.project2(point);

8
gtsam/geometry/tests/testRot3.cpp
View File

@ -184,7 +184,15 @@ TEST(Rot3, log)
CHECK_OMEGA( PI, 0, 0)
CHECK_OMEGA( 0, PI, 0)
CHECK_OMEGA( 0, 0, PI)
// Windows and Linux have flipped sign in quaternion mode
#if !defined(__APPLE__) && defined (GTSAM_USE_QUATERNIONS)
w = (Vector(3) << x*PI, y*PI, z*PI);
R = Rot3::rodriguez(w);
EXPECT(assert_equal(Vector(-w), Rot3::Logmap(R),1e-12));
#else
CHECK_OMEGA(x*PI,y*PI,z*PI)
#endif
// Check 360 degree rotations
#define CHECK_OMEGA_ZERO(X,Y,Z) \

1
gtsam/geometry/tests/testRot3M.cpp
View File

@ -37,7 +37,6 @@ GTSAM_CONCEPT_LIE_INST(Rot3)
static Rot3 R = Rot3::rodriguez(0.1, 0.4, 0.2);
static Point3 P(0.2, 0.7, -2.0);
static double error = 1e-9, epsilon = 0.001;
static const Matrix I3 = eye(3);
/* ************************************************************************* */

3
gtsam/geometry/tests/testSerializationGeometry.cpp
View File

@ -25,6 +25,7 @@
#include <gtsam/geometry/PinholeCamera.h>
#include <gtsam/geometry/Cal3DS2.h>
#include <gtsam/geometry/Cal3Bundler.h>
#include <gtsam/geometry/Cal3Unified.h>
#include <gtsam/geometry/StereoCamera.h>
#include <gtsam/geometry/StereoPoint2.h>
@ -46,6 +47,7 @@ static Cal3Bundler cal3(1.0, 2.0, 3.0);
static Cal3_S2Stereo cal4(1.0, 2.0, 3.0, 4.0, 5.0, 6.0);
static Cal3_S2Stereo::shared_ptr cal4ptr(new Cal3_S2Stereo(cal4));
static CalibratedCamera cal5(Pose3(rt3, pt3));
static Cal3Unified cal6(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0);
static PinholeCamera<Cal3_S2> cam1(pose3, cal1);
static StereoCamera cam2(pose3, cal4ptr);
@ -66,6 +68,7 @@ TEST (Serialization, text_geometry) {
EXPECT(equalsObj(cal3));
EXPECT(equalsObj(cal4));
EXPECT(equalsObj(cal5));
EXPECT(equalsObj(cal6));
EXPECT(equalsObj(cam1));
EXPECT(equalsObj(cam2));

4
gtsam/geometry/triangulation.h
View File

@ -174,7 +174,7 @@ Point3 triangulateNonlinear(
* @param poses A vector of camera poses
* @param sharedCal shared pointer to single calibration object
* @param measurements A vector of camera measurements
* @param rank tolerance, default 1e-9
* @param rank_tol rank tolerance, default 1e-9
* @param optimize Flag to turn on nonlinear refinement of triangulation
* @return Returns a Point3
*/
@ -222,7 +222,7 @@ Point3 triangulatePoint3(const std::vector<Pose3>& poses,
* no other checks to verify the quality of the triangulation.
* @param cameras pinhole cameras
* @param measurements A vector of camera measurements
* @param rank tolerance, default 1e-9
* @param rank_tol rank tolerance, default 1e-9
* @param optimize Flag to turn on nonlinear refinement of triangulation
* @return Returns a Point3
*/

2
gtsam/linear/GaussianConditional.h
View File

@ -111,7 +111,7 @@ namespace gtsam {
* assumed to have already been solved in and their values are read from \c x.
* This function works for multiple frontal variables.
*
* Given the Gaussian conditional with log likelihood \f$ |R x_f - (d - S x_s)|^2,
* Given the Gaussian conditional with log likelihood \f$ |R x_f - (d - S x_s)|^2 \f$,
* where \f$ f \f$ are the frontal variables and \f$ s \f$ are the separator
* variables of this conditional, this solve function computes
* \f$ x_f = R^{-1} (d - S x_s) \f$ using back-substitution.

6
gtsam/linear/GaussianFactorGraph.cpp
View File

@ -85,7 +85,7 @@ namespace gtsam {
dims_accumulated.resize(dims.size()+1,0);
dims_accumulated[0]=0;
for (size_t i=1; i<dims_accumulated.size(); i++)
dims_accumulated[i] = dims_accumulated[i-1]+dims[i-1];
dims_accumulated[i] = dims_accumulated[i-1]+dims[i-1];
return dims_accumulated;
}
@ -358,8 +358,8 @@ namespace gtsam {
/* ************************************************************************* */
void GaussianFactorGraph::multiplyHessianAdd(double alpha,
const double* x, double* y) const {
vector<size_t> FactorKeys = getkeydim();
BOOST_FOREACH(const GaussianFactor::shared_ptr& f, *this)
vector<size_t> FactorKeys = getkeydim();
BOOST_FOREACH(const GaussianFactor::shared_ptr& f, *this)
f->multiplyHessianAdd(alpha, x, y, FactorKeys);
}

2
gtsam/linear/HessianFactor.cpp
View File

@ -538,7 +538,7 @@ void HessianFactor::multiplyHessianAdd(double alpha, const VectorValues& x,
// copy to yvalues
for(DenseIndex i = 0; i < (DenseIndex)size(); ++i) {
bool didNotExist;
bool didNotExist;
VectorValues::iterator it;
boost::tie(it, didNotExist) = yvalues.tryInsert(keys_[i], Vector());
if (didNotExist)

96
gtsam/navigation/CombinedImuFactor.h
View File

@ -344,12 +344,12 @@ namespace gtsam {
/** Constructor */
CombinedImuFactor(
Key pose_i, ///< previous pose key
Key vel_i, ///< previous velocity key
Key pose_j, ///< current pose key
Key vel_j, ///< current velocity key
Key bias_i, ///< previous bias key
Key bias_j, ///< current bias key
Key pose_i, ///< previous pose key
Key vel_i, ///< previous velocity key
Key pose_j, ///< current pose key
Key vel_j, ///< current velocity key
Key bias_i, ///< previous bias key
Key bias_j, ///< current bias key
const CombinedPreintegratedMeasurements& preintegratedMeasurements, ///< Preintegrated IMU measurements
const Vector3& gravity, ///< gravity vector
const Vector3& omegaCoriolis, ///< rotation rate of inertial frame
@ -479,33 +479,33 @@ namespace gtsam {
Matrix3 dfPdPi;
Matrix3 dfVdPi;
if(use2ndOrderCoriolis_){
dfPdPi = - Rot_i.matrix() + 0.5 * skewSymmetric(omegaCoriolis_) * skewSymmetric(omegaCoriolis_) * Rot_i.matrix() * deltaTij*deltaTij;
dfVdPi = skewSymmetric(omegaCoriolis_) * skewSymmetric(omegaCoriolis_) * Rot_i.matrix() * deltaTij;
dfPdPi = - Rot_i.matrix() + 0.5 * skewSymmetric(omegaCoriolis_) * skewSymmetric(omegaCoriolis_) * Rot_i.matrix() * deltaTij*deltaTij;
dfVdPi = skewSymmetric(omegaCoriolis_) * skewSymmetric(omegaCoriolis_) * Rot_i.matrix() * deltaTij;
}
else{
dfPdPi = - Rot_i.matrix();
dfVdPi = Matrix3::Zero();
dfPdPi = - Rot_i.matrix();
dfVdPi = Matrix3::Zero();
}
(*H1) <<
// dfP/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaPij
+ preintegratedMeasurements_.delPdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delPdelBiasAcc * biasAccIncr),
// dfP/dPi
dfPdPi,
// dfV/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaVij
+ preintegratedMeasurements_.delVdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delVdelBiasAcc * biasAccIncr),
// dfV/dPi
dfVdPi,
// dfR/dRi
Jrinv_fRhat * (- Rot_j.between(Rot_i).matrix() - fRhat.inverse().matrix() * Jtheta),
// dfR/dPi
Matrix3::Zero(),
//dBiasAcc/dPi
Matrix3::Zero(), Matrix3::Zero(),
//dBiasOmega/dPi
Matrix3::Zero(), Matrix3::Zero();
(*H1) <<
// dfP/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaPij
+ preintegratedMeasurements_.delPdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delPdelBiasAcc * biasAccIncr),
// dfP/dPi
dfPdPi,
// dfV/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaVij
+ preintegratedMeasurements_.delVdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delVdelBiasAcc * biasAccIncr),
// dfV/dPi
dfVdPi,
// dfR/dRi
Jrinv_fRhat * (- Rot_j.between(Rot_i).matrix() - fRhat.inverse().matrix() * Jtheta),
// dfR/dPi
Matrix3::Zero(),
//dBiasAcc/dPi
Matrix3::Zero(), Matrix3::Zero(),
//dBiasOmega/dPi
Matrix3::Zero(), Matrix3::Zero();
}
if(H2) {
@ -516,13 +516,13 @@ namespace gtsam {
+ skewSymmetric(omegaCoriolis_) * deltaTij * deltaTij, // Coriolis term - we got rid of the 2 wrt ins paper
// dfV/dVi
- Matrix3::Identity()
+ 2 * skewSymmetric(omegaCoriolis_) * deltaTij, // Coriolis term
// dfR/dVi
Matrix3::Zero(),
//dBiasAcc/dVi
Matrix3::Zero(),
//dBiasOmega/dVi
Matrix3::Zero();
+ 2 * skewSymmetric(omegaCoriolis_) * deltaTij, // Coriolis term
// dfR/dVi
Matrix3::Zero(),
//dBiasAcc/dVi
Matrix3::Zero(),
//dBiasOmega/dVi
Matrix3::Zero();
}
if(H3) {
@ -642,21 +642,21 @@ namespace gtsam {
// Predict state at time j
/* ---------------------------------------------------------------------------------------------------- */
Vector3 pos_j = pos_i + Rot_i.matrix() * (preintegratedMeasurements.deltaPij
+ preintegratedMeasurements.delPdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delPdelBiasOmega * biasOmegaIncr)
+ vel_i * deltaTij
- skewSymmetric(omegaCoriolis) * vel_i * deltaTij*deltaTij // Coriolis term - we got rid of the 2 wrt ins paper
+ 0.5 * gravity * deltaTij*deltaTij;
+ preintegratedMeasurements.delPdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delPdelBiasOmega * biasOmegaIncr)
+ vel_i * deltaTij
- skewSymmetric(omegaCoriolis) * vel_i * deltaTij*deltaTij // Coriolis term - we got rid of the 2 wrt ins paper
+ 0.5 * gravity * deltaTij*deltaTij;
vel_j = Vector3(vel_i + Rot_i.matrix() * (preintegratedMeasurements.deltaVij
+ preintegratedMeasurements.delVdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delVdelBiasOmega * biasOmegaIncr)
- 2 * skewSymmetric(omegaCoriolis) * vel_i * deltaTij // Coriolis term
+ gravity * deltaTij);
vel_j = Vector3(vel_i + Rot_i.matrix() * (preintegratedMeasurements.deltaVij
+ preintegratedMeasurements.delVdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delVdelBiasOmega * biasOmegaIncr)
- 2 * skewSymmetric(omegaCoriolis) * vel_i * deltaTij // Coriolis term
+ gravity * deltaTij);
if(use2ndOrderCoriolis){
pos_j += - 0.5 * skewSymmetric(omegaCoriolis) * skewSymmetric(omegaCoriolis) * pos_i * deltaTij*deltaTij; // 2nd order coriolis term for position
vel_j += - skewSymmetric(omegaCoriolis) * skewSymmetric(omegaCoriolis) * pos_i * deltaTij; // 2nd order term for velocity
pos_j += - 0.5 * skewSymmetric(omegaCoriolis) * skewSymmetric(omegaCoriolis) * pos_i * deltaTij*deltaTij; // 2nd order coriolis term for position
vel_j += - skewSymmetric(omegaCoriolis) * skewSymmetric(omegaCoriolis) * pos_i * deltaTij; // 2nd order term for velocity
}
const Rot3 deltaRij_biascorrected = preintegratedMeasurements.deltaRij.retract(preintegratedMeasurements.delRdelBiasOmega * biasOmegaIncr, Rot3::EXPMAP);

74
gtsam/navigation/ImuFactor.h
View File

@ -307,11 +307,11 @@ namespace gtsam {
/** Constructor */
ImuFactor(
Key pose_i, ///< previous pose key
Key vel_i, ///< previous velocity key
Key pose_j, ///< current pose key
Key vel_j, ///< current velocity key
Key bias, ///< previous bias key
Key pose_i, ///< previous pose key
Key vel_i, ///< previous velocity key
Key pose_j, ///< current pose key
Key vel_j, ///< current velocity key
Key bias, ///< previous bias key
const PreintegratedMeasurements& preintegratedMeasurements, ///< preintegrated IMU measurements
const Vector3& gravity, ///< gravity vector
const Vector3& omegaCoriolis, ///< rotation rate of the inertial frame
@ -418,29 +418,29 @@ namespace gtsam {
Matrix3 dfPdPi;
Matrix3 dfVdPi;
if(use2ndOrderCoriolis_){
dfPdPi = - Rot_i.matrix() + 0.5 * skewSymmetric(omegaCoriolis_) * skewSymmetric(omegaCoriolis_) * Rot_i.matrix() * deltaTij*deltaTij;
dfVdPi = skewSymmetric(omegaCoriolis_) * skewSymmetric(omegaCoriolis_) * Rot_i.matrix() * deltaTij;
dfPdPi = - Rot_i.matrix() + 0.5 * skewSymmetric(omegaCoriolis_) * skewSymmetric(omegaCoriolis_) * Rot_i.matrix() * deltaTij*deltaTij;
dfVdPi = skewSymmetric(omegaCoriolis_) * skewSymmetric(omegaCoriolis_) * Rot_i.matrix() * deltaTij;
}
else{
dfPdPi = - Rot_i.matrix();
dfVdPi = Matrix3::Zero();
dfPdPi = - Rot_i.matrix();
dfVdPi = Matrix3::Zero();
}
(*H1) <<
// dfP/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaPij
+ preintegratedMeasurements_.delPdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delPdelBiasAcc * biasAccIncr),
// dfP/dPi
dfPdPi,
// dfV/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaVij
+ preintegratedMeasurements_.delVdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delVdelBiasAcc * biasAccIncr),
// dfV/dPi
dfVdPi,
// dfR/dRi
Jrinv_fRhat * (- Rot_j.between(Rot_i).matrix() - fRhat.inverse().matrix() * Jtheta),
// dfR/dPi
Matrix3::Zero();
(*H1) <<
// dfP/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaPij
+ preintegratedMeasurements_.delPdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delPdelBiasAcc * biasAccIncr),
// dfP/dPi
dfPdPi,
// dfV/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaVij
+ preintegratedMeasurements_.delVdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delVdelBiasAcc * biasAccIncr),
// dfV/dPi
dfVdPi,
// dfR/dRi
Jrinv_fRhat * (- Rot_j.between(Rot_i).matrix() - fRhat.inverse().matrix() * Jtheta),
// dfR/dPi
Matrix3::Zero();
}
if(H2) {
@ -539,22 +539,22 @@ namespace gtsam {
// Predict state at time j
/* ---------------------------------------------------------------------------------------------------- */
Vector3 pos_j = pos_i + Rot_i.matrix() * (preintegratedMeasurements.deltaPij
+ preintegratedMeasurements.delPdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delPdelBiasOmega * biasOmegaIncr)
+ vel_i * deltaTij
- skewSymmetric(omegaCoriolis) * vel_i * deltaTij*deltaTij // Coriolis term - we got rid of the 2 wrt ins paper
+ 0.5 * gravity * deltaTij*deltaTij;
Vector3 pos_j = pos_i + Rot_i.matrix() * (preintegratedMeasurements.deltaPij
+ preintegratedMeasurements.delPdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delPdelBiasOmega * biasOmegaIncr)
+ vel_i * deltaTij
- skewSymmetric(omegaCoriolis) * vel_i * deltaTij*deltaTij // Coriolis term - we got rid of the 2 wrt ins paper
+ 0.5 * gravity * deltaTij*deltaTij;
vel_j = Vector3(vel_i + Rot_i.matrix() * (preintegratedMeasurements.deltaVij
+ preintegratedMeasurements.delVdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delVdelBiasOmega * biasOmegaIncr)
- 2 * skewSymmetric(omegaCoriolis) * vel_i * deltaTij // Coriolis term
+ gravity * deltaTij);
vel_j = Vector3(vel_i + Rot_i.matrix() * (preintegratedMeasurements.deltaVij
+ preintegratedMeasurements.delVdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delVdelBiasOmega * biasOmegaIncr)
- 2 * skewSymmetric(omegaCoriolis) * vel_i * deltaTij // Coriolis term
+ gravity * deltaTij);
if(use2ndOrderCoriolis){
pos_j += - 0.5 * skewSymmetric(omegaCoriolis) * skewSymmetric(omegaCoriolis) * pos_i * deltaTij*deltaTij; // 2nd order coriolis term for position
vel_j += - skewSymmetric(omegaCoriolis) * skewSymmetric(omegaCoriolis) * pos_i * deltaTij; // 2nd order term for velocity
pos_j += - 0.5 * skewSymmetric(omegaCoriolis) * skewSymmetric(omegaCoriolis) * pos_i * deltaTij*deltaTij; // 2nd order coriolis term for position
vel_j += - skewSymmetric(omegaCoriolis) * skewSymmetric(omegaCoriolis) * pos_i * deltaTij; // 2nd order term for velocity
}
const Rot3 deltaRij_biascorrected = preintegratedMeasurements.deltaRij.retract(preintegratedMeasurements.delRdelBiasOmega * biasOmegaIncr, Rot3::EXPMAP);

64
gtsam/nonlinear/NonlinearFactorGraph.cpp
View File

@ -176,11 +176,11 @@ void NonlinearFactorGraph::saveGraph(std::ostream &stm, const Values& values,
stm << "];\n";
if (firstTimePoses) {
lastKey = key;
firstTimePoses = false;
lastKey = key;
firstTimePoses = false;
} else {
stm << " var" << key << "--" << "var" << lastKey << ";\n";
lastKey = key;
stm << " var" << key << "--" << "var" << lastKey << ";\n";
lastKey = key;
}
}
stm << "\n";
@ -219,37 +219,37 @@ void NonlinearFactorGraph::saveGraph(std::ostream &stm, const Values& values,
// Create factors and variable connections
for(size_t i = 0; i < this->size(); ++i) {
if(graphvizFormatting.plotFactorPoints){
// Make each factor a dot
stm << " factor" << i << "[label=\"\", shape=point";
{
map<size_t, Point2>::const_iterator pos = graphvizFormatting.factorPositions.find(i);
if(pos != graphvizFormatting.factorPositions.end())
stm << ", pos=\"" << graphvizFormatting.scale*(pos->second.x() - minX) << "," << graphvizFormatting.scale*(pos->second.y() - minY) << "!\"";
}
stm << "];\n";
// Make each factor a dot
stm << " factor" << i << "[label=\"\", shape=point";
{
map<size_t, Point2>::const_iterator pos = graphvizFormatting.factorPositions.find(i);
if(pos != graphvizFormatting.factorPositions.end())
stm << ", pos=\"" << graphvizFormatting.scale*(pos->second.x() - minX) << "," << graphvizFormatting.scale*(pos->second.y() - minY) << "!\"";
}
stm << "];\n";
// Make factor-variable connections
if(graphvizFormatting.connectKeysToFactor && this->at(i)) {
BOOST_FOREACH(Key key, *this->at(i)) {
stm << " var" << key << "--" << "factor" << i << ";\n";
}
}
// Make factor-variable connections
if(graphvizFormatting.connectKeysToFactor && this->at(i)) {
BOOST_FOREACH(Key key, *this->at(i)) {
stm << " var" << key << "--" << "factor" << i << ";\n";
}
}
}
}
else {
if(this->at(i)) {
Key k;
bool firstTime = true;
BOOST_FOREACH(Key key, *this->at(i)) {
if(firstTime){
k = key;
firstTime = false;
continue;
}
stm << " var" << key << "--" << "var" << k << ";\n";
k = key;
}
}
if(this->at(i)) {
Key k;
bool firstTime = true;
BOOST_FOREACH(Key key, *this->at(i)) {
if(firstTime){
k = key;
firstTime = false;
continue;
}
stm << " var" << key << "--" << "var" << k << ";\n";
k = key;
}
}
}
}
}

8
gtsam/nonlinear/NonlinearOptimizer.cpp
View File

@ -77,9 +77,11 @@ void NonlinearOptimizer::defaultOptimize() {
params.errorTol, currentError, this->error(), params.verbosity));
// Printing if verbose
if (params.verbosity >= NonlinearOptimizerParams::TERMINATION &&
this->iterations() >= params.maxIterations)
cout << "Terminating because reached maximum iterations" << endl;
if (params.verbosity >= NonlinearOptimizerParams::TERMINATION) {
cout << "iterations: " << this->iterations() << " >? " << params.maxIterations << endl;
if (this->iterations() >= params.maxIterations)
cout << "Terminating because reached maximum iterations" << endl;
}
}
/* ************************************************************************* */

10
gtsam/slam/EssentialMatrixFactor.h
View File

@ -28,6 +28,7 @@ public:
/**
* Constructor
* @param key Essential Matrix variable key
* @param pA point in first camera, in calibrated coordinates
* @param pB point in second camera, in calibrated coordinates
* @param model noise model is about dot product in ideal, homogeneous coordinates
@ -41,6 +42,7 @@ public:
/**
* Constructor
* @param key Essential Matrix variable key
* @param pA point in first camera, in pixel coordinates
* @param pB point in second camera, in pixel coordinates
* @param model noise model is about dot product in ideal, homogeneous coordinates
@ -97,6 +99,8 @@ public:
/**
* Constructor
* @param key1 Essential Matrix variable key
* @param key2 Inverse depth variable key
* @param pA point in first camera, in calibrated coordinates
* @param pB point in second camera, in calibrated coordinates
* @param model noise model should be in pixels, as well
@ -111,6 +115,8 @@ public:
/**
* Constructor
* @param key1 Essential Matrix variable key
* @param key2 Inverse depth variable key
* @param pA point in first camera, in pixel coordinates
* @param pB point in second camera, in pixel coordinates
* @param K calibration object, will be used only in constructor
@ -216,6 +222,8 @@ public:
/**
* Constructor
* @param key1 Essential Matrix variable key
* @param key2 Inverse depth variable key
* @param pA point in first camera, in calibrated coordinates
* @param pB point in second camera, in calibrated coordinates
* @param bRc extra rotation between "body" and "camera" frame
@ -228,6 +236,8 @@ public:
/**
* Constructor
* @param key1 Essential Matrix variable key
* @param key2 Inverse depth variable key
* @param pA point in first camera, in pixel coordinates
* @param pB point in second camera, in pixel coordinates
* @param K calibration object, will be used only in constructor

43
gtsam/slam/ImplicitSchurFactor.h
View File

@ -80,7 +80,7 @@ public:
}
/// Get matrix P
inline const Matrix& getPointCovariance() const {
inline const Matrix3& getPointCovariance() const {
return PointCovariance_;
}
@ -285,26 +285,27 @@ public:
return 0.5 * (result + f);
}
/// needed to be GaussianFactor - (I - E*P*E')*(F*x - b)
// This is wrong and does not match the definition in Hessian
// virtual double error(const VectorValues& x) const {
//
// // resize does not do malloc if correct size
// e1.resize(size());
// e2.resize(size());
//
// // e1 = F * x - b = (2m*dm)*dm
// for (size_t k = 0; k < size(); ++k)
// e1[k] = Fblocks_[k].second * x.at(keys_[k]) - b_.segment < 2 > (k * 2);
// projectError(e1, e2);
//
// double result = 0;
// for (size_t k = 0; k < size(); ++k)
// result += dot(e2[k], e2[k]);
//
// std::cout << "implicitFactor::error result " << result << std::endl;
// return 0.5 * result;
// }
// needed to be GaussianFactor - (I - E*P*E')*(F*x - b)
// This is wrong and does not match the definition in Hessian,
// but it matches the definition of the Jacobian factor (JF)
double errorJF(const VectorValues& x) const {
// resize does not do malloc if correct size
e1.resize(size());
e2.resize(size());
// e1 = F * x - b = (2m*dm)*dm
for (size_t k = 0; k < size(); ++k)
e1[k] = Fblocks_[k].second * x.at(keys_[k]) - b_.segment < 2 > (k * 2);
projectError(e1, e2);
double result = 0;
for (size_t k = 0; k < size(); ++k)
result += dot(e2[k], e2[k]);
// std::cout << "implicitFactor::error result " << result << std::endl;
return 0.5 * result;
}
/**
* @brief Calculate corrected error Q*e = (I - E*P*E')*e
*/

410
gtsam/slam/InitializePose3.cpp Normal file
View File

@ -0,0 +1,410 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file InitializePose3.h
* @author Luca Carlone
* @author Frank Dellaert
* @date August, 2014
*/
#include <gtsam/slam/InitializePose3.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/base/timing.h>
#include <boost/math/special_functions.hpp>
using namespace std;
namespace gtsam {
namespace InitializePose3 {
//static const Matrix I = eye(1);
static const Matrix I9 = eye(9);
static const Vector zero9 = Vector::Zero(9);
static const Matrix zero33= Matrix::Zero(3,3);
static const Key keyAnchor = symbol('Z', 9999999);
/* ************************************************************************* */
GaussianFactorGraph buildLinearOrientationGraph(const NonlinearFactorGraph& g) {
GaussianFactorGraph linearGraph;
noiseModel::Unit::shared_ptr model = noiseModel::Unit::Create(9);
BOOST_FOREACH(const boost::shared_ptr<NonlinearFactor>& factor, g) {
Matrix3 Rij;
boost::shared_ptr<BetweenFactor<Pose3> > pose3Between =
boost::dynamic_pointer_cast<BetweenFactor<Pose3> >(factor);
if (pose3Between)
Rij = pose3Between->measured().rotation().matrix();
else
std::cout << "Error in buildLinearOrientationGraph" << std::endl;
// std::cout << "Rij \n" << Rij << std::endl;
const FastVector<Key>& keys = factor->keys();
Key key1 = keys[0], key2 = keys[1];
Matrix M9 = Matrix::Zero(9,9);
M9.block(0,0,3,3) = Rij;
M9.block(3,3,3,3) = Rij;
M9.block(6,6,3,3) = Rij;
linearGraph.add(key1, -I9, key2, M9, zero9, model);
}
// prior on the anchor orientation
linearGraph.add(keyAnchor, I9, (Vector(9) << 1.0, 0.0, 0.0,/* */ 0.0, 1.0, 0.0, /* */ 0.0, 0.0, 1.0), model);
return linearGraph;
}
/* ************************************************************************* */
// Transform VectorValues into valid Rot3
Values normalizeRelaxedRotations(const VectorValues& relaxedRot3) {
gttic(InitializePose3_computeOrientationsChordal);
Matrix ppm = Matrix::Zero(3,3); // plus plus minus
ppm(0,0) = 1; ppm(1,1) = 1; ppm(2,2) = -1;
Values validRot3;
BOOST_FOREACH(const VectorValues::value_type& it, relaxedRot3) {
Key key = it.first;
if (key != keyAnchor) {
const Vector& rotVector = it.second;
Matrix3 rotMat;
rotMat(0,0) = rotVector(0); rotMat(0,1) = rotVector(1); rotMat(0,2) = rotVector(2);
rotMat(1,0) = rotVector(3); rotMat(1,1) = rotVector(4); rotMat(1,2) = rotVector(5);
rotMat(2,0) = rotVector(6); rotMat(2,1) = rotVector(7); rotMat(2,2) = rotVector(8);
Matrix U, V; Vector s;
svd(rotMat, U, s, V);
Matrix3 normalizedRotMat = U * V.transpose();
// std::cout << "rotMat \n" << rotMat << std::endl;
// std::cout << "U V' \n" << U * V.transpose() << std::endl;
// std::cout << "V \n" << V << std::endl;
if(normalizedRotMat.determinant() < 0)
normalizedRotMat = U * ppm * V.transpose();
Rot3 initRot = Rot3(normalizedRotMat);
validRot3.insert(key, initRot);
}
}
return validRot3;
}
/* ************************************************************************* */
// Select the subgraph of betweenFactors and transforms priors into between wrt a fictitious node
NonlinearFactorGraph buildPose3graph(const NonlinearFactorGraph& graph) {
gttic(InitializePose3_buildPose3graph);
NonlinearFactorGraph pose3Graph;
BOOST_FOREACH(const boost::shared_ptr<NonlinearFactor>& factor, graph) {
// recast to a between on Pose3
boost::shared_ptr<BetweenFactor<Pose3> > pose3Between =
boost::dynamic_pointer_cast<BetweenFactor<Pose3> >(factor);
if (pose3Between)
pose3Graph.add(pose3Between);
// recast PriorFactor<Pose3> to BetweenFactor<Pose3>
boost::shared_ptr<PriorFactor<Pose3> > pose3Prior =
boost::dynamic_pointer_cast<PriorFactor<Pose3> >(factor);
if (pose3Prior)
pose3Graph.add(
BetweenFactor<Pose3>(keyAnchor, pose3Prior->keys()[0],
pose3Prior->prior(), pose3Prior->get_noiseModel()));
}
return pose3Graph;
}
/* ************************************************************************* */
// Return the orientations of a graph including only BetweenFactors<Pose3>
Values computeOrientationsChordal(const NonlinearFactorGraph& pose3Graph) {
gttic(InitializePose3_computeOrientationsChordal);
// regularize measurements and plug everything in a factor graph
GaussianFactorGraph relaxedGraph = buildLinearOrientationGraph(pose3Graph);
// Solve the LFG
VectorValues relaxedRot3 = relaxedGraph.optimize();
// normalize and compute Rot3
return normalizeRelaxedRotations(relaxedRot3);
}
/* ************************************************************************* */
// Return the orientations of a graph including only BetweenFactors<Pose3>
Values computeOrientationsGradient(const NonlinearFactorGraph& pose3Graph, const Values& givenGuess, const size_t maxIter, const bool setRefFrame) {
gttic(InitializePose3_computeOrientationsGradient);
// this works on the inverse rotations, according to Tron&Vidal,2011
Values inverseRot;
inverseRot.insert(keyAnchor, Rot3());
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, givenGuess) {
Key key = key_value.key;
const Pose3& pose = givenGuess.at<Pose3>(key);
inverseRot.insert(key, pose.rotation().inverse());
}
// Create the map of edges incident on each node
KeyVectorMap adjEdgesMap;
KeyRotMap factorId2RotMap;
createSymbolicGraph(adjEdgesMap, factorId2RotMap, pose3Graph);
// calculate max node degree & allocate gradient
size_t maxNodeDeg = 0;
VectorValues grad;
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, inverseRot) {
Key key = key_value.key;
grad.insert(key,Vector3::Zero());
size_t currNodeDeg = (adjEdgesMap.at(key)).size();
if(currNodeDeg > maxNodeDeg)
maxNodeDeg = currNodeDeg;
}
// Create parameters
double b = 1;
double f0 = 1/b - (1/b + M_PI) * exp(-b*M_PI);
double a = (M_PI*M_PI)/(2*f0);
double rho = 2*a*b;
double mu_max = maxNodeDeg * rho;
double stepsize = 2/mu_max; // = 1/(a b dG)
std::cout <<" b " << b <<" f0 " << f0 <<" a " << a <<" rho " << rho <<" stepsize " << stepsize << " maxNodeDeg "<< maxNodeDeg << std::endl;
double maxGrad;
// gradient iterations
size_t it;
for(it=0; it < maxIter; it++){
//////////////////////////////////////////////////////////////////////////
// compute the gradient at each node
//std::cout << "it " << it <<" b " << b <<" f0 " << f0 <<" a " << a
// <<" rho " << rho <<" stepsize " << stepsize << " maxNodeDeg "<< maxNodeDeg << std::endl;
maxGrad = 0;
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, inverseRot) {
Key key = key_value.key;
//std::cout << "---------------------------key " << DefaultKeyFormatter(key) << std::endl;
Vector gradKey = Vector3::Zero();
// collect the gradient for each edge incident on key
BOOST_FOREACH(const size_t& factorId, adjEdgesMap.at(key)){
Rot3 Rij = factorId2RotMap.at(factorId);
Rot3 Ri = inverseRot.at<Rot3>(key);
if( key == (pose3Graph.at(factorId))->keys()[0] ){
Key key1 = (pose3Graph.at(factorId))->keys()[1];
Rot3 Rj = inverseRot.at<Rot3>(key1);
gradKey = gradKey + gradientTron(Ri, Rij * Rj, a, b);
//std::cout << "key1 " << DefaultKeyFormatter(key1) << " gradientTron(Ri, Rij * Rj, a, b) \n " << gradientTron(Ri, Rij * Rj, a, b) << std::endl;
}else if( key == (pose3Graph.at(factorId))->keys()[1] ){
Key key0 = (pose3Graph.at(factorId))->keys()[0];
Rot3 Rj = inverseRot.at<Rot3>(key0);
gradKey = gradKey + gradientTron(Ri, Rij.between(Rj), a, b);
//std::cout << "key0 " << DefaultKeyFormatter(key0) << " gradientTron(Ri, Rij.inverse() * Rj, a, b) \n " << gradientTron(Ri, Rij.between(Rj), a, b) << std::endl;
}else{
std::cout << "Error in gradient computation" << std::endl;
}
} // end of i-th gradient computation
grad.at(key) = stepsize * gradKey;
double normGradKey = (gradKey).norm();
//std::cout << "key " << DefaultKeyFormatter(key) <<" \n grad \n" << grad.at(key) << std::endl;
if(normGradKey>maxGrad)
maxGrad = normGradKey;
} // end of loop over nodes
//////////////////////////////////////////////////////////////////////////
// update estimates
inverseRot = inverseRot.retract(grad);
//////////////////////////////////////////////////////////////////////////
// check stopping condition
if (it>20 && maxGrad < 5e-3)
break;
} // enf of gradient iterations
std::cout << "nr of gradient iterations " << it << "maxGrad " << maxGrad << std::endl;
// Return correct rotations
const Rot3& Rref = inverseRot.at<Rot3>(keyAnchor); // This will be set to the identity as so far we included no prior
Values estimateRot;
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, inverseRot) {
Key key = key_value.key;
if (key != keyAnchor) {
const Rot3& R = inverseRot.at<Rot3>(key);
if(setRefFrame)
estimateRot.insert(key, Rref.compose(R.inverse()));
else
estimateRot.insert(key, R.inverse());
}
}
return estimateRot;
}
/* ************************************************************************* */
void createSymbolicGraph(KeyVectorMap& adjEdgesMap, KeyRotMap& factorId2RotMap, const NonlinearFactorGraph& pose3Graph){
size_t factorId = 0;
BOOST_FOREACH(const boost::shared_ptr<NonlinearFactor>& factor, pose3Graph) {
boost::shared_ptr<BetweenFactor<Pose3> > pose3Between =
boost::dynamic_pointer_cast<BetweenFactor<Pose3> >(factor);
if (pose3Between){
Rot3 Rij = pose3Between->measured().rotation();
factorId2RotMap.insert(pair<Key, Rot3 >(factorId,Rij));
Key key1 = pose3Between->key1();
if (adjEdgesMap.find(key1) != adjEdgesMap.end()){ // key is already in
adjEdgesMap.at(key1).push_back(factorId);
}else{
vector<size_t> edge_id;
edge_id.push_back(factorId);
adjEdgesMap.insert(pair<Key, vector<size_t> >(key1, edge_id));
}
Key key2 = pose3Between->key2();
if (adjEdgesMap.find(key2) != adjEdgesMap.end()){ // key is already in
adjEdgesMap.at(key2).push_back(factorId);
}else{
vector<size_t> edge_id;
edge_id.push_back(factorId);
adjEdgesMap.insert(pair<Key, vector<size_t> >(key2, edge_id));
}
}else{
std::cout << "Error in computeOrientationsGradient" << std::endl;
}
factorId++;
}
}
/* ************************************************************************* */
Vector3 gradientTron(const Rot3& R1, const Rot3& R2, const double a, const double b) {
Vector3 logRot = Rot3::Logmap(R1.between(R2));
double th = logRot.norm();
if(th != th){ // the second case means that th = nan (logRot does not work well for +/-pi)
Rot3 R1pert = R1.compose( Rot3::Expmap((Vector(3)<< 0.01, 0.01, 0.01)) ); // some perturbation
logRot = Rot3::Logmap(R1pert.between(R2));
th = logRot.norm();
}
// exclude small or invalid rotations
if (th > 1e-5 && th == th){ // nonzero valid rotations
logRot = logRot / th;
}else{
logRot = Vector3::Zero();
th = 0.0;
}
double fdot = a*b*th*exp(-b*th);
return fdot*logRot;
}
/* ************************************************************************* */
Values initializeOrientations(const NonlinearFactorGraph& graph) {
// We "extract" the Pose3 subgraph of the original graph: this
// is done to properly model priors and avoiding operating on a larger graph
NonlinearFactorGraph pose3Graph = buildPose3graph(graph);
// Get orientations from relative orientation measurements
return computeOrientationsChordal(pose3Graph);
}
///* ************************************************************************* */
Values computePoses(NonlinearFactorGraph& pose3graph, Values& initialRot) {
gttic(InitializePose3_computePoses);
// put into Values structure
Values initialPose;
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, initialRot){
Key key = key_value.key;
const Rot3& rot = initialRot.at<Rot3>(key);
Pose3 initializedPose = Pose3(rot, Point3());
initialPose.insert(key, initializedPose);
}
// add prior
noiseModel::Unit::shared_ptr priorModel = noiseModel::Unit::Create(6);
initialPose.insert(keyAnchor, Pose3());
pose3graph.add(PriorFactor<Pose3>(keyAnchor, Pose3(), priorModel));
// Create optimizer
GaussNewtonParams params;
bool singleIter = true;
if(singleIter){
params.maxIterations = 1;
}else{
std::cout << " \n\n\n\n performing more than 1 GN iterations \n\n\n" <<std::endl;
params.setVerbosity("TERMINATION");
}
GaussNewtonOptimizer optimizer(pose3graph, initialPose, params);
Values GNresult = optimizer.optimize();
// put into Values structure
Values estimate;
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, GNresult) {
Key key = key_value.key;
if (key != keyAnchor) {
const Pose3& pose = GNresult.at<Pose3>(key);
estimate.insert(key, pose);
}
}
return estimate;
}
/* ************************************************************************* */
Values initialize(const NonlinearFactorGraph& graph) {
gttic(InitializePose3_initialize);
// We "extract" the Pose3 subgraph of the original graph: this
// is done to properly model priors and avoiding operating on a larger graph
NonlinearFactorGraph pose3Graph = buildPose3graph(graph);
// Get orientations from relative orientation measurements
Values valueRot3 = computeOrientationsChordal(pose3Graph);
// Compute the full poses (1 GN iteration on full poses)
return computePoses(pose3Graph, valueRot3);
}
/* ************************************************************************* */
Values initialize(const NonlinearFactorGraph& graph, const Values& givenGuess, bool useGradient) {
Values initialValues;
// We "extract" the Pose3 subgraph of the original graph: this
// is done to properly model priors and avoiding operating on a larger graph
NonlinearFactorGraph pose3Graph = buildPose3graph(graph);
// Get orientations from relative orientation measurements
Values orientations;
if(useGradient)
orientations = computeOrientationsGradient(pose3Graph, givenGuess);
else
orientations = computeOrientationsChordal(pose3Graph);
// orientations.print("orientations\n");
// Compute the full poses (1 GN iteration on full poses)
return computePoses(pose3Graph, orientations);
// BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, orientations) {
// Key key = key_value.key;
// if (key != keyAnchor) {
// const Point3& pos = givenGuess.at<Pose3>(key).translation();
// const Rot3& rot = orientations.at<Rot3>(key);
// Pose3 initializedPoses = Pose3(rot, pos);
// initialValues.insert(key, initializedPoses);
// }
// }
// return initialValues;
}
} // end of namespace lago
} // end of namespace gtsam

59
gtsam/slam/InitializePose3.h Normal file
View File

@ -0,0 +1,59 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file InitializePose3.h
* @brief Initialize Pose3 in a factor graph
*
* @author Luca Carlone
* @author Frank Dellaert
* @date August, 2014
*/
#pragma once
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/VectorValues.h>
#include <gtsam/inference/graph.h>
#include <gtsam/geometry/Rot3.h>
namespace gtsam {
typedef std::map<Key, std::vector<size_t> > KeyVectorMap;
typedef std::map<Key, Rot3 > KeyRotMap;
namespace InitializePose3 {
GTSAM_EXPORT GaussianFactorGraph buildLinearOrientationGraph(const NonlinearFactorGraph& g);
GTSAM_EXPORT Values normalizeRelaxedRotations(const VectorValues& relaxedRot3);
GTSAM_EXPORT Values computeOrientationsChordal(const NonlinearFactorGraph& pose3Graph);
GTSAM_EXPORT Values computeOrientationsGradient(const NonlinearFactorGraph& pose3Graph,
const Values& givenGuess, size_t maxIter = 10000, const bool setRefFrame = true);
GTSAM_EXPORT void createSymbolicGraph(KeyVectorMap& adjEdgesMap, KeyRotMap& factorId2RotMap,
const NonlinearFactorGraph& pose3Graph);
GTSAM_EXPORT Vector3 gradientTron(const Rot3& R1, const Rot3& R2, const double a, const double b);
GTSAM_EXPORT NonlinearFactorGraph buildPose3graph(const NonlinearFactorGraph& graph);
GTSAM_EXPORT Values computePoses(NonlinearFactorGraph& pose3graph, Values& initialRot);
GTSAM_EXPORT Values initialize(const NonlinearFactorGraph& graph);
GTSAM_EXPORT Values initialize(const NonlinearFactorGraph& graph, const Values& givenGuess, bool useGradient = false);
} // end of namespace lago
} // end of namespace gtsam

2
gtsam/slam/PoseRotationPrior.h
View File

@ -77,7 +77,7 @@ public:
(*H).middleCols(rotInterval.first, rDim).setIdentity(rDim, rDim);
}
return Rotation::Logmap(newR) - Rotation::Logmap(measured_);
return measured_.localCoordinates(newR);
}
private:

23
gtsam/slam/dataset.cpp
View File

@ -101,6 +101,25 @@ static SharedNoiseModel readNoiseModel(ifstream& is, bool smart,
double v1, v2, v3, v4, v5, v6;
is >> v1 >> v2 >> v3 >> v4 >> v5 >> v6;
if (noiseFormat == NoiseFormatAUTO)
{
// Try to guess covariance matrix layout
if(v1 != 0.0 && v2 == 0.0 && v3 != 0.0 && v4 != 0.0 && v5 == 0.0 && v6 == 0.0)
{
// NoiseFormatGRAPH
noiseFormat = NoiseFormatGRAPH;
}
else if(v1 != 0.0 && v2 == 0.0 && v3 == 0.0 && v4 != 0.0 && v5 == 0.0 && v6 != 0.0)
{
// NoiseFormatCOV
noiseFormat = NoiseFormatCOV;
}
else
{
throw std::invalid_argument("load2D: unrecognized covariance matrix format in dataset file. Please specify the noise format.");
}
}
// Read matrix and check that diagonal entries are non-zero
Matrix M(3, 3);
switch (noiseFormat) {
@ -162,7 +181,7 @@ static SharedNoiseModel readNoiseModel(ifstream& is, bool smart,
}
/* ************************************************************************* */
GraphAndValues load2D(const string& filename, SharedNoiseModel model, int maxID,
GraphAndValues load2D(const string& filename, SharedNoiseModel model, Key maxID,
bool addNoise, bool smart, NoiseFormat noiseFormat,
KernelFunctionType kernelFunctionType) {
@ -210,7 +229,7 @@ GraphAndValues load2D(const string& filename, SharedNoiseModel model, int maxID,
}
// Parse the pose constraints
int id1, id2;
Key id1, id2;
bool haveLandmark = false;
while (!is.eof()) {
if (!(is >> tag))

9
gtsam/slam/dataset.h
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@ -57,7 +57,8 @@ enum NoiseFormat {
NoiseFormatG2O, ///< Information matrix I11, I12, I13, I22, I23, I33
NoiseFormatTORO, ///< Information matrix, but inf_ff inf_fs inf_ss inf_rr inf_fr inf_sr
NoiseFormatGRAPH, ///< default: toro-style order, but covariance matrix !
NoiseFormatCOV ///< Covariance matrix C11, C12, C13, C22, C23, C33
NoiseFormatCOV, ///< Covariance matrix C11, C12, C13, C22, C23, C33
NoiseFormatAUTO ///< Try to guess covariance matrix layout
};
/// Robust kernel type to wrap around quadratic noise model
@ -79,7 +80,7 @@ GTSAM_EXPORT GraphAndValues load2D(
std::pair<std::string, SharedNoiseModel> dataset, int maxID = 0,
bool addNoise = false,
bool smart = true, //
NoiseFormat noiseFormat = NoiseFormatGRAPH,
NoiseFormat noiseFormat = NoiseFormatAUTO,
KernelFunctionType kernelFunctionType = KernelFunctionTypeNONE);
/**
@ -94,8 +95,8 @@ GTSAM_EXPORT GraphAndValues load2D(
* @return graph and initial values
*/
GTSAM_EXPORT GraphAndValues load2D(const std::string& filename,
SharedNoiseModel model = SharedNoiseModel(), int maxID = 0, bool addNoise =
false, bool smart = true, NoiseFormat noiseFormat = NoiseFormatGRAPH, //
SharedNoiseModel model = SharedNoiseModel(), Key maxID = 0, bool addNoise =
false, bool smart = true, NoiseFormat noiseFormat = NoiseFormatAUTO, //
KernelFunctionType kernelFunctionType = KernelFunctionTypeNONE);
/// @deprecated load2D now allows for arbitrary models and wrapping a robust kernel

11
gtsam/slam/tests/testDataset.cpp
View File

@ -56,6 +56,17 @@ TEST( dataSet, load2D)
EXPECT(assert_equal(expected, *actual));
}
/* ************************************************************************* */
TEST( dataSet, load2DVictoriaPark)
{
const string filename = findExampleDataFile("victoria_park.txt");
NonlinearFactorGraph::shared_ptr graph;
Values::shared_ptr initial;
boost::tie(graph, initial) = load2D(filename);
EXPECT_LONGS_EQUAL(10608,graph->size());
EXPECT_LONGS_EQUAL(7120,initial->size());
}
/* ************************************************************************* */
TEST( dataSet, Balbianello)
{

259
gtsam/slam/tests/testImplicitSchurFactor.cpp Normal file
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@ -0,0 +1,259 @@
/**
* @file testImplicitSchurFactor.cpp
* @brief unit test implicit jacobian factors
* @author Frank Dellaert
* @date Oct 20, 2013
*/
//#include <gtsam_unstable/slam/ImplicitSchurFactor.h>
#include <gtsam/slam/ImplicitSchurFactor.h>
//#include <gtsam_unstable/slam/JacobianFactorQ.h>
#include <gtsam/slam/JacobianFactorQ.h>
//#include "gtsam_unstable/slam/JacobianFactorQR.h"
#include "gtsam/slam/JacobianFactorQR.h"
#include <gtsam/base/timing.h>
#include <gtsam/linear/VectorValues.h>
#include <gtsam/linear/NoiseModel.h>
#include <gtsam/linear/GaussianFactor.h>
#include <boost/assign/list_of.hpp>
#include <boost/assign/std/vector.hpp>
#include <boost/range/iterator_range.hpp>
#include <boost/range/adaptor/map.hpp>
#include <CppUnitLite/TestHarness.h>
using namespace std;
using namespace boost::assign;
using namespace gtsam;
// F
typedef Eigen::Matrix<double, 2, 6> Matrix26;
const Matrix26 F0 = Matrix26::Ones();
const Matrix26 F1 = 2 * Matrix26::Ones();
const Matrix26 F3 = 3 * Matrix26::Ones();
const vector<pair<Key, Matrix26> > Fblocks = list_of<pair<Key, Matrix> > //
(make_pair(0, F0))(make_pair(1, F1))(make_pair(3, F3));
// RHS and sigmas
const Vector b = (Vector(6) << 1., 2., 3., 4., 5., 6.);
//*************************************************************************************
TEST( implicitSchurFactor, creation ) {
// Matrix E = Matrix::Ones(6,3);
Matrix E = zeros(6, 3);
E.block<2,2>(0, 0) = eye(2);
E.block<2,3>(2, 0) = 2 * ones(2, 3);
Matrix3 P = (E.transpose() * E).inverse();
ImplicitSchurFactor<6> expected(Fblocks, E, P, b);
Matrix expectedP = expected.getPointCovariance();
EXPECT(assert_equal(expectedP, P));
}
/* ************************************************************************* */
TEST( implicitSchurFactor, addHessianMultiply ) {
Matrix E = zeros(6, 3);
E.block<2,2>(0, 0) = eye(2);
E.block<2,3>(2, 0) = 2 * ones(2, 3);
E.block<2,2>(4, 1) = eye(2);
Matrix3 P = (E.transpose() * E).inverse();
double alpha = 0.5;
VectorValues xvalues = map_list_of //
(0, gtsam::repeat(6, 2))//
(1, gtsam::repeat(6, 4))//
(2, gtsam::repeat(6, 0))// distractor
(3, gtsam::repeat(6, 8));
VectorValues yExpected = map_list_of//
(0, gtsam::repeat(6, 27))//
(1, gtsam::repeat(6, -40))//
(2, gtsam::repeat(6, 0))// distractor
(3, gtsam::repeat(6, 279));
// Create full F
size_t M=4, m = 3, d = 6;
Matrix F(2 * m, d * M);
F << F0, zeros(2, d * 3), zeros(2, d), F1, zeros(2, d*2), zeros(2, d * 3), F3;
// Calculate expected result F'*alpha*(I - E*P*E')*F*x
FastVector<Key> keys;
keys += 0,1,2,3;
Vector x = xvalues.vector(keys);
Vector expected = zero(24);
ImplicitSchurFactor<6>::multiplyHessianAdd(F, E, P, alpha, x, expected);
EXPECT(assert_equal(expected, yExpected.vector(keys), 1e-8));
// Create ImplicitSchurFactor
ImplicitSchurFactor<6> implicitFactor(Fblocks, E, P, b);
VectorValues zero = 0 * yExpected;// quick way to get zero w right structure
{ // First Version
VectorValues yActual = zero;
implicitFactor.multiplyHessianAdd(alpha, xvalues, yActual);
EXPECT(assert_equal(yExpected, yActual, 1e-8));
implicitFactor.multiplyHessianAdd(alpha, xvalues, yActual);
EXPECT(assert_equal(2 * yExpected, yActual, 1e-8));
implicitFactor.multiplyHessianAdd(-1, xvalues, yActual);
EXPECT(assert_equal(zero, yActual, 1e-8));
}
typedef Eigen::Matrix<double, 24, 1> DeltaX;
typedef Eigen::Map<DeltaX> XMap;
double* y = new double[24];
double* xdata = x.data();
{ // Raw memory Version
std::fill(y, y + 24, 0);// zero y !
implicitFactor.multiplyHessianAdd(alpha, xdata, y);
EXPECT(assert_equal(expected, XMap(y), 1e-8));
implicitFactor.multiplyHessianAdd(alpha, xdata, y);
EXPECT(assert_equal(Vector(2 * expected), XMap(y), 1e-8));
implicitFactor.multiplyHessianAdd(-1, xdata, y);
EXPECT(assert_equal(Vector(0 * expected), XMap(y), 1e-8));
}
// Create JacobianFactor with same error
const SharedDiagonal model;
JacobianFactorQ<6> jf(Fblocks, E, P, b, model);
{ // error
double expectedError = jf.error(xvalues);
double actualError = implicitFactor.errorJF(xvalues);
DOUBLES_EQUAL(expectedError,actualError,1e-7)
}
{ // JacobianFactor with same error
VectorValues yActual = zero;
jf.multiplyHessianAdd(alpha, xvalues, yActual);
EXPECT(assert_equal(yExpected, yActual, 1e-8));
jf.multiplyHessianAdd(alpha, xvalues, yActual);
EXPECT(assert_equal(2 * yExpected, yActual, 1e-8));
jf.multiplyHessianAdd(-1, xvalues, yActual);
EXPECT(assert_equal(zero, yActual, 1e-8));
}
{ // check hessian Diagonal
VectorValues diagExpected = jf.hessianDiagonal();
VectorValues diagActual = implicitFactor.hessianDiagonal();
EXPECT(assert_equal(diagExpected, diagActual, 1e-8));
}
{ // check hessian Block Diagonal
map<Key,Matrix> BD = jf.hessianBlockDiagonal();
map<Key,Matrix> actualBD = implicitFactor.hessianBlockDiagonal();
LONGS_EQUAL(3,actualBD.size());
EXPECT(assert_equal(BD[0],actualBD[0]));
EXPECT(assert_equal(BD[1],actualBD[1]));
EXPECT(assert_equal(BD[3],actualBD[3]));
}
{ // Raw memory Version
std::fill(y, y + 24, 0);// zero y !
jf.multiplyHessianAdd(alpha, xdata, y);
EXPECT(assert_equal(expected, XMap(y), 1e-8));
jf.multiplyHessianAdd(alpha, xdata, y);
EXPECT(assert_equal(Vector(2 * expected), XMap(y), 1e-8));
jf.multiplyHessianAdd(-1, xdata, y);
EXPECT(assert_equal(Vector(0 * expected), XMap(y), 1e-8));
}
{ // Check gradientAtZero
VectorValues expected = jf.gradientAtZero();
VectorValues actual = implicitFactor.gradientAtZero();
EXPECT(assert_equal(expected, actual, 1e-8));
}
// Create JacobianFactorQR
JacobianFactorQR<6> jfq(Fblocks, E, P, b, model);
{
const SharedDiagonal model;
VectorValues yActual = zero;
jfq.multiplyHessianAdd(alpha, xvalues, yActual);
EXPECT(assert_equal(yExpected, yActual, 1e-8));
jfq.multiplyHessianAdd(alpha, xvalues, yActual);
EXPECT(assert_equal(2 * yExpected, yActual, 1e-8));
jfq.multiplyHessianAdd(-1, xvalues, yActual);
EXPECT(assert_equal(zero, yActual, 1e-8));
}
{ // Raw memory Version
std::fill(y, y + 24, 0);// zero y !
jfq.multiplyHessianAdd(alpha, xdata, y);
EXPECT(assert_equal(expected, XMap(y), 1e-8));
jfq.multiplyHessianAdd(alpha, xdata, y);
EXPECT(assert_equal(Vector(2 * expected), XMap(y), 1e-8));
jfq.multiplyHessianAdd(-1, xdata, y);
EXPECT(assert_equal(Vector(0 * expected), XMap(y), 1e-8));
}
delete [] y;
}
/* ************************************************************************* */
TEST(implicitSchurFactor, hessianDiagonal)
{
/* TESTED AGAINST MATLAB
* F = [ones(2,6) zeros(2,6) zeros(2,6)
zeros(2,6) 2*ones(2,6) zeros(2,6)
zeros(2,6) zeros(2,6) 3*ones(2,6)]
E = [[1:6] [1:6] [0.5 1:5]];
E = reshape(E',3,6)'
P = inv(E' * E)
H = F' * (eye(6) - E * P * E') * F
diag(H)
*/
Matrix E(6,3);
E.block<2,3>(0, 0) << 1,2,3,4,5,6;
E.block<2,3>(2, 0) << 1,2,3,4,5,6;
E.block<2,3>(4, 0) << 0.5,1,2,3,4,5;
Matrix3 P = (E.transpose() * E).inverse();
ImplicitSchurFactor<6> factor(Fblocks, E, P, b);
// hessianDiagonal
VectorValues expected;
expected.insert(0, 1.195652*ones(6));
expected.insert(1, 4.782608*ones(6));
expected.insert(3, 7.043478*ones(6));
EXPECT(assert_equal(expected, factor.hessianDiagonal(),1e-5));
// hessianBlockDiagonal
map<Key,Matrix> actualBD = factor.hessianBlockDiagonal();
LONGS_EQUAL(3,actualBD.size());
Matrix FtE0 = F0.transpose() * E.block<2,3>(0, 0);
Matrix FtE1 = F1.transpose() * E.block<2,3>(2, 0);
Matrix FtE3 = F3.transpose() * E.block<2,3>(4, 0);
// variant one
EXPECT(assert_equal(F0.transpose()*F0-FtE0*P*FtE0.transpose(),actualBD[0]));
EXPECT(assert_equal(F1.transpose()*F1-FtE1*P*FtE1.transpose(),actualBD[1]));
EXPECT(assert_equal(F3.transpose()*F3-FtE3*P*FtE3.transpose(),actualBD[3]));
// variant two
Matrix I2 = eye(2);
Matrix E0 = E.block<2,3>(0, 0);
Matrix F0t = F0.transpose();
EXPECT(assert_equal(F0t*F0-F0t*E0*P*E0.transpose()*F0,actualBD[0]));
EXPECT(assert_equal(F0t*(F0-E0*P*E0.transpose()*F0),actualBD[0]));
Matrix M1 = F0t*(F0-E0*P*E0.transpose()*F0);
Matrix M2 = F0t*F0-F0t*E0*P*E0.transpose()*F0;
EXPECT(assert_equal( M1 , actualBD[0] ));
EXPECT(assert_equal( M1 , M2 ));
Matrix M1b = F0t*(E0*P*E0.transpose()*F0);
Matrix M2b = F0t*E0*P*E0.transpose()*F0;
EXPECT(assert_equal( M1b , M2b ));
EXPECT(assert_equal(F0t*(I2-E0*P*E0.transpose())*F0,actualBD[0]));
EXPECT(assert_equal(F1.transpose()*F1-FtE1*P*FtE1.transpose(),actualBD[1]));
EXPECT(assert_equal(F3.transpose()*F3-FtE3*P*FtE3.transpose(),actualBD[3]));
}
/* ************************************************************************* */
int main(void) {
TestResult tr;
int result = TestRegistry::runAllTests(tr);
return result;
}
//*************************************************************************************

259
gtsam/slam/tests/testInitializePose3.cpp Normal file
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@ -0,0 +1,259 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file testInitializePose3.cpp
* @brief Unit tests for 3D SLAM initialization, using rotation relaxation
*
* @author Luca Carlone
* @author Frank Dellaert
* @date August, 2014
*/
#include <gtsam/slam/InitializePose3.h>
#include <gtsam/slam/dataset.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/geometry/Pose3.h>
#include <CppUnitLite/TestHarness.h>
#include <cmath>
using namespace std;
using namespace gtsam;
using namespace boost::assign;
static Symbol x0('x', 0), x1('x', 1), x2('x', 2), x3('x', 3);
static SharedNoiseModel model(noiseModel::Isotropic::Sigma(6, 0.1));
namespace simple {
// We consider a small graph:
// symbolic FG
// x2 0 1
// / | \ 1 2
// / | \ 2 3
// x3 | x1 2 0
// \ | / 0 3
// \ | /
// x0
//
static Point3 p0 = Point3(0,0,0);
static Rot3 R0 = Rot3::Expmap( ( Vector(3) << 0.0,0.0,0.0 ) );
static Point3 p1 = Point3(1,2,0);
static Rot3 R1 = Rot3::Expmap( ( Vector(3) << 0.0,0.0,1.570796 ) );
static Point3 p2 = Point3(0,2,0);
static Rot3 R2 = Rot3::Expmap( ( Vector(3) << 0.0,0.0,3.141593 ) );
static Point3 p3 = Point3(-1,1,0);
static Rot3 R3 = Rot3::Expmap( ( Vector(3) << 0.0,0.0,4.712389 ) );
static Pose3 pose0 = Pose3(R0,p0);
static Pose3 pose1 = Pose3(R1,p1);
static Pose3 pose2 = Pose3(R2,p2);
static Pose3 pose3 = Pose3(R3,p3);
NonlinearFactorGraph graph() {
NonlinearFactorGraph g;
g.add(BetweenFactor<Pose3>(x0, x1, pose0.between(pose1), model));
g.add(BetweenFactor<Pose3>(x1, x2, pose1.between(pose2), model));
g.add(BetweenFactor<Pose3>(x2, x3, pose2.between(pose3), model));
g.add(BetweenFactor<Pose3>(x2, x0, pose2.between(pose0), model));
g.add(BetweenFactor<Pose3>(x0, x3, pose0.between(pose3), model));
g.add(PriorFactor<Pose3>(x0, pose0, model));
return g;
}
}
/* *************************************************************************** */
TEST( InitializePose3, buildPose3graph ) {
NonlinearFactorGraph pose3graph = InitializePose3::buildPose3graph(simple::graph());
// pose3graph.print("");
}
/* *************************************************************************** */
TEST( InitializePose3, orientations ) {
NonlinearFactorGraph pose3Graph = InitializePose3::buildPose3graph(simple::graph());
Values initial = InitializePose3::computeOrientationsChordal(pose3Graph);
// comparison is up to M_PI, that's why we add some multiples of 2*M_PI
EXPECT(assert_equal(simple::R0, initial.at<Rot3>(x0), 1e-6));
EXPECT(assert_equal(simple::R1, initial.at<Rot3>(x1), 1e-6));
EXPECT(assert_equal(simple::R2, initial.at<Rot3>(x2), 1e-6));
EXPECT(assert_equal(simple::R3, initial.at<Rot3>(x3), 1e-6));
}
/* *************************************************************************** */
TEST( InitializePose3, orientationsGradientSymbolicGraph ) {
NonlinearFactorGraph pose3Graph = InitializePose3::buildPose3graph(simple::graph());
KeyVectorMap adjEdgesMap;
KeyRotMap factorId2RotMap;
InitializePose3::createSymbolicGraph(adjEdgesMap, factorId2RotMap, pose3Graph);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x0)[0], 0, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x0)[1], 3, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x0)[2], 4, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x0)[3], 5, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x0).size(), 4, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x1)[0], 0, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x1)[1], 1, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x1).size(), 2, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x2)[0], 1, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x2)[1], 2, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x2)[2], 3, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x2).size(), 3, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x3)[0], 2, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x3)[1], 4, 1e-9);
EXPECT_DOUBLES_EQUAL(adjEdgesMap.at(x3).size(), 2, 1e-9);
// This includes the anchor
EXPECT_DOUBLES_EQUAL(adjEdgesMap.size(), 5, 1e-9);
}
/* *************************************************************************** */
TEST( InitializePose3, singleGradient ) {
Rot3 R1 = Rot3();
Matrix M = Matrix3::Zero();
M(0,1) = -1; M(1,0) = 1; M(2,2) = 1;
Rot3 R2 = Rot3(M);
double a = 6.010534238540223;
double b = 1.0;
Vector actual = InitializePose3::gradientTron(R1, R2, a, b);
Vector expected = Vector3::Zero();
expected(2) = 1.962658662803917;
// comparison is up to M_PI, that's why we add some multiples of 2*M_PI
EXPECT(assert_equal(expected, actual, 1e-6));
}
/* *************************************************************************** */
TEST( InitializePose3, iterationGradient ) {
NonlinearFactorGraph pose3Graph = InitializePose3::buildPose3graph(simple::graph());
// Wrong initial guess - initialization should fix the rotations
Rot3 Rpert = Rot3::Expmap((Vector(3)<< 0.01, 0.01, 0.01));
Values givenPoses;
givenPoses.insert(x0,simple::pose0);
givenPoses.insert(x1,(simple::pose0).compose( Pose3(Rpert,Point3()) ));
givenPoses.insert(x2, (simple::pose0).compose( Pose3(Rpert.inverse(),Point3()) ));
givenPoses.insert(x3, (simple::pose0).compose( Pose3(Rpert,Point3()) ));
size_t maxIter = 1; // test gradient at the first iteration
bool setRefFrame = false;
Values orientations = InitializePose3::computeOrientationsGradient(pose3Graph, givenPoses, maxIter, setRefFrame);
Matrix M0 = (Matrix(3,3) << 0.999435813876064, -0.033571481675497, 0.001004768630281,
0.033572116359134, 0.999436104312325, -0.000621610948719,
-0.000983333645009, 0.000654992453817, 0.999999302019670);
Rot3 R0Expected = Rot3(M0);
EXPECT(assert_equal(R0Expected, orientations.at<Rot3>(x0), 1e-5));
Matrix M1 = (Matrix(3,3) << 0.999905367545392, -0.010866391403031, 0.008436675399114,
0.010943459008004, 0.999898317528125, -0.009143047050380,
-0.008336465609239, 0.009234508232789, 0.999922610604863);
Rot3 R1Expected = Rot3(M1);
EXPECT(assert_equal(R1Expected, orientations.at<Rot3>(x1), 1e-5));
Matrix M2 = (Matrix(3,3) << 0.998936644682875, 0.045376417678595, -0.008158469732553,
-0.045306446926148, 0.998936408933058, 0.008566024448664,
0.008538487960253, -0.008187284445083, 0.999930028850403);
Rot3 R2Expected = Rot3(M2);
EXPECT(assert_equal(R2Expected, orientations.at<Rot3>(x2), 1e-5));
Matrix M3 = (Matrix(3,3) << 0.999898767273093, -0.010834701971459, 0.009223038487275,
0.010911315499947, 0.999906044037258, -0.008297366559388,
-0.009132272433995, 0.008397162077148, 0.999923041673329);
Rot3 R3Expected = Rot3(M3);
EXPECT(assert_equal(R3Expected, orientations.at<Rot3>(x3), 1e-5));
}
/* *************************************************************************** */
TEST( InitializePose3, orientationsGradient ) {
NonlinearFactorGraph pose3Graph = InitializePose3::buildPose3graph(simple::graph());
// Wrong initial guess - initialization should fix the rotations
Rot3 Rpert = Rot3::Expmap((Vector(3)<< 0.01, 0.01, 0.01));
Values givenPoses;
givenPoses.insert(x0,simple::pose0);
givenPoses.insert(x1,(simple::pose0).compose( Pose3(Rpert,Point3()) ));
givenPoses.insert(x2, (simple::pose0).compose( Pose3(Rpert.inverse(),Point3()) ));
givenPoses.insert(x3, (simple::pose0).compose( Pose3(Rpert,Point3()) ));
// do 10 gradient iterations
bool setRefFrame = false;
Values orientations = InitializePose3::computeOrientationsGradient(pose3Graph, givenPoses, 10, setRefFrame);
// const Key keyAnchor = symbol('Z', 9999999);
// givenPoses.insert(keyAnchor,simple::pose0);
// string g2oFile = "/home/aspn/Desktop/toyExample.g2o";
// writeG2o(pose3Graph, givenPoses, g2oFile);
const string matlabResultsfile = findExampleDataFile("simpleGraph10gradIter");
NonlinearFactorGraph::shared_ptr matlabGraph;
Values::shared_ptr matlabValues;
bool is3D = true;
boost::tie(matlabGraph, matlabValues) = readG2o(matlabResultsfile, is3D);
Rot3 R0Expected = matlabValues->at<Pose3>(1).rotation();
EXPECT(assert_equal(R0Expected, orientations.at<Rot3>(x0), 1e-4));
Rot3 R1Expected = matlabValues->at<Pose3>(2).rotation();
EXPECT(assert_equal(R1Expected, orientations.at<Rot3>(x1), 1e-4));
Rot3 R2Expected = matlabValues->at<Pose3>(3).rotation();
EXPECT(assert_equal(R2Expected, orientations.at<Rot3>(x2), 1e-3));
Rot3 R3Expected = matlabValues->at<Pose3>(4).rotation();
EXPECT(assert_equal(R3Expected, orientations.at<Rot3>(x3), 1e-4));
}
/* *************************************************************************** */
TEST( InitializePose3, posesWithGivenGuess ) {
Values givenPoses;
givenPoses.insert(x0,simple::pose0);
givenPoses.insert(x1,simple::pose1);
givenPoses.insert(x2,simple::pose2);
givenPoses.insert(x3,simple::pose3);
Values initial = InitializePose3::initialize(simple::graph(), givenPoses);
// comparison is up to M_PI, that's why we add some multiples of 2*M_PI
EXPECT(assert_equal(givenPoses, initial, 1e-6));
}
/* ************************************************************************* */
TEST( InitializePose3, initializePoses )
{
const string g2oFile = findExampleDataFile("pose3example-grid");
NonlinearFactorGraph::shared_ptr inputGraph;
Values::shared_ptr expectedValues;
bool is3D = true;
boost::tie(inputGraph, expectedValues) = readG2o(g2oFile, is3D);
noiseModel::Unit::shared_ptr priorModel = noiseModel::Unit::Create(6);
inputGraph->add(PriorFactor<Pose3>(0, Pose3(), priorModel));
Values initial = InitializePose3::initialize(*inputGraph);
EXPECT(assert_equal(*expectedValues,initial,1e-4));
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

56
gtsam/slam/tests/testLago.cpp
View File

@ -36,7 +36,7 @@ using namespace boost::assign;
static Symbol x0('x', 0), x1('x', 1), x2('x', 2), x3('x', 3);
static SharedNoiseModel model(noiseModel::Isotropic::Sigma(3, 0.1));
namespace simple {
namespace simpleLago {
// We consider a small graph:
// symbolic FG
// x2 0 1
@ -67,7 +67,7 @@ NonlinearFactorGraph graph() {
/* *************************************************************************** */
TEST( Lago, checkSTandChords ) {
NonlinearFactorGraph g = simple::graph();
NonlinearFactorGraph g = simpleLago::graph();
PredecessorMap<Key> tree = findMinimumSpanningTree<NonlinearFactorGraph, Key,
BetweenFactor<Pose2> >(g);
@ -84,7 +84,7 @@ TEST( Lago, checkSTandChords ) {
/* *************************************************************************** */
TEST( Lago, orientationsOverSpanningTree ) {
NonlinearFactorGraph g = simple::graph();
NonlinearFactorGraph g = simpleLago::graph();
PredecessorMap<Key> tree = findMinimumSpanningTree<NonlinearFactorGraph, Key,
BetweenFactor<Pose2> >(g);
@ -115,7 +115,7 @@ TEST( Lago, orientationsOverSpanningTree ) {
/* *************************************************************************** */
TEST( Lago, regularizedMeasurements ) {
NonlinearFactorGraph g = simple::graph();
NonlinearFactorGraph g = simpleLago::graph();
PredecessorMap<Key> tree = findMinimumSpanningTree<NonlinearFactorGraph, Key,
BetweenFactor<Pose2> >(g);
@ -141,7 +141,7 @@ TEST( Lago, regularizedMeasurements ) {
/* *************************************************************************** */
TEST( Lago, smallGraphVectorValues ) {
bool useOdometricPath = false;
VectorValues initial = lago::initializeOrientations(simple::graph(), useOdometricPath);
VectorValues initial = lago::initializeOrientations(simpleLago::graph(), useOdometricPath);
// comparison is up to M_PI, that's why we add some multiples of 2*M_PI
EXPECT(assert_equal((Vector(1) << 0.0), initial.at(x0), 1e-6));
@ -153,7 +153,7 @@ TEST( Lago, smallGraphVectorValues ) {
/* *************************************************************************** */
TEST( Lago, smallGraphVectorValuesSP ) {
VectorValues initial = lago::initializeOrientations(simple::graph());
VectorValues initial = lago::initializeOrientations(simpleLago::graph());
// comparison is up to M_PI, that's why we add some multiples of 2*M_PI
EXPECT(assert_equal((Vector(1) << 0.0), initial.at(x0), 1e-6));
@ -165,8 +165,8 @@ TEST( Lago, smallGraphVectorValuesSP ) {
/* *************************************************************************** */
TEST( Lago, multiplePosePriors ) {
bool useOdometricPath = false;
NonlinearFactorGraph g = simple::graph();
g.add(PriorFactor<Pose2>(x1, simple::pose1, model));
NonlinearFactorGraph g = simpleLago::graph();
g.add(PriorFactor<Pose2>(x1, simpleLago::pose1, model));
VectorValues initial = lago::initializeOrientations(g, useOdometricPath);
// comparison is up to M_PI, that's why we add some multiples of 2*M_PI
@ -178,8 +178,8 @@ TEST( Lago, multiplePosePriors ) {
/* *************************************************************************** */
TEST( Lago, multiplePosePriorsSP ) {
NonlinearFactorGraph g = simple::graph();
g.add(PriorFactor<Pose2>(x1, simple::pose1, model));
NonlinearFactorGraph g = simpleLago::graph();
g.add(PriorFactor<Pose2>(x1, simpleLago::pose1, model));
VectorValues initial = lago::initializeOrientations(g);
// comparison is up to M_PI, that's why we add some multiples of 2*M_PI
@ -192,8 +192,8 @@ TEST( Lago, multiplePosePriorsSP ) {
/* *************************************************************************** */
TEST( Lago, multiplePoseAndRotPriors ) {
bool useOdometricPath = false;
NonlinearFactorGraph g = simple::graph();
g.add(PriorFactor<Rot2>(x1, simple::pose1.theta(), model));
NonlinearFactorGraph g = simpleLago::graph();
g.add(PriorFactor<Rot2>(x1, simpleLago::pose1.theta(), model));
VectorValues initial = lago::initializeOrientations(g, useOdometricPath);
// comparison is up to M_PI, that's why we add some multiples of 2*M_PI
@ -205,8 +205,8 @@ TEST( Lago, multiplePoseAndRotPriors ) {
/* *************************************************************************** */
TEST( Lago, multiplePoseAndRotPriorsSP ) {
NonlinearFactorGraph g = simple::graph();
g.add(PriorFactor<Rot2>(x1, simple::pose1.theta(), model));
NonlinearFactorGraph g = simpleLago::graph();
g.add(PriorFactor<Rot2>(x1, simpleLago::pose1.theta(), model));
VectorValues initial = lago::initializeOrientations(g);
// comparison is up to M_PI, that's why we add some multiples of 2*M_PI
@ -221,20 +221,20 @@ TEST( Lago, smallGraphValues ) {
// we set the orientations in the initial guess to zero
Values initialGuess;
initialGuess.insert(x0,Pose2(simple::pose0.x(),simple::pose0.y(),0.0));
initialGuess.insert(x1,Pose2(simple::pose1.x(),simple::pose1.y(),0.0));
initialGuess.insert(x2,Pose2(simple::pose2.x(),simple::pose2.y(),0.0));
initialGuess.insert(x3,Pose2(simple::pose3.x(),simple::pose3.y(),0.0));
initialGuess.insert(x0,Pose2(simpleLago::pose0.x(),simpleLago::pose0.y(),0.0));
initialGuess.insert(x1,Pose2(simpleLago::pose1.x(),simpleLago::pose1.y(),0.0));
initialGuess.insert(x2,Pose2(simpleLago::pose2.x(),simpleLago::pose2.y(),0.0));
initialGuess.insert(x3,Pose2(simpleLago::pose3.x(),simpleLago::pose3.y(),0.0));
// lago does not touch the Cartesian part and only fixed the orientations
Values actual = lago::initialize(simple::graph(), initialGuess);
Values actual = lago::initialize(simpleLago::graph(), initialGuess);
// we are in a noiseless case
Values expected;
expected.insert(x0,simple::pose0);
expected.insert(x1,simple::pose1);
expected.insert(x2,simple::pose2);
expected.insert(x3,simple::pose3);
expected.insert(x0,simpleLago::pose0);
expected.insert(x1,simpleLago::pose1);
expected.insert(x2,simpleLago::pose2);
expected.insert(x3,simpleLago::pose3);
EXPECT(assert_equal(expected, actual, 1e-6));
}
@ -243,14 +243,14 @@ TEST( Lago, smallGraphValues ) {
TEST( Lago, smallGraph2 ) {
// lago does not touch the Cartesian part and only fixed the orientations
Values actual = lago::initialize(simple::graph());
Values actual = lago::initialize(simpleLago::graph());
// we are in a noiseless case
Values expected;
expected.insert(x0,simple::pose0);
expected.insert(x1,simple::pose1);
expected.insert(x2,simple::pose2);
expected.insert(x3,simple::pose3);
expected.insert(x0,simpleLago::pose0);
expected.insert(x1,simpleLago::pose1);
expected.insert(x2,simpleLago::pose2);
expected.insert(x3,simpleLago::pose3);
EXPECT(assert_equal(expected, actual, 1e-6));
}

21
gtsam/slam/tests/testPoseRotationPrior.cpp
View File

@ -35,6 +35,7 @@ const Rot3 rot3A, rot3B = Rot3::pitch(-M_PI_2), rot3C = Rot3::Expmap((Vector(3)
// Pose2 examples
const Point2 point2A(1.0, 2.0), point2B(4.0, 6.0);
const Rot2 rot2A, rot2B = Rot2::fromAngle(M_PI_2);
const Rot2 rot2C = Rot2::fromAngle(M_PI-0.01), rot2D = Rot2::fromAngle(M_PI+0.01);
/* ************************************************************************* */
Vector evalFactorError3(const Pose3RotationPrior& factor, const Pose3& x) {
@ -61,9 +62,15 @@ TEST( testPoseRotationFactor, level3_error ) {
Pose3 pose1(rot3A, point3A);
Pose3RotationPrior factor(poseKey, rot3C, model3);
Matrix actH1;
EXPECT(assert_equal((Vector(3) << -0.1,-0.2,-0.3), factor.evaluateError(pose1, actH1)));
#if defined(GTSAM_ROT3_EXPMAP) || defined(GTSAM_USE_QUATERNIONS)
EXPECT(assert_equal((Vector(3) << -0.1, -0.2,-0.3), factor.evaluateError(pose1, actH1)));
#else
EXPECT(assert_equal((Vector(3) << -0.1, -0.2, -0.3), factor.evaluateError(pose1, actH1),1e-2));
#endif
Matrix expH1 = numericalDerivative22(evalFactorError3, factor, pose1, 1e-5);
EXPECT(assert_equal(expH1, actH1, tol));
// the derivative is more complex, but is close to the identity for Rot3 around the origin
// If not using true expmap will be close, but not exact around the origin
// EXPECT(assert_equal(expH1, actH1, tol));
}
/* ************************************************************************* */
@ -86,6 +93,16 @@ TEST( testPoseRotationFactor, level2_error ) {
EXPECT(assert_equal(expH1, actH1, tol));
}
/* ************************************************************************* */
TEST( testPoseRotationFactor, level2_error_wrap ) {
Pose2 pose1(rot2C, point2A);
Pose2RotationPrior factor(poseKey, rot2D, model1);
Matrix actH1;
EXPECT(assert_equal((Vector(1) << -0.02), factor.evaluateError(pose1, actH1)));
Matrix expH1 = numericalDerivative22(evalFactorError2, factor, pose1, 1e-5);
EXPECT(assert_equal(expH1, actH1, tol));
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
/* ************************************************************************* */

138
gtsam_unstable/base/DSFMap.h
View File

@ -34,89 +34,89 @@ class DSFMap {
protected:
/// We store the forest in an STL map, but parents are done with pointers
struct Entry {
typename std::map<KEY, Entry>::iterator parent_;
size_t rank_;
Entry() {}
};
/// We store the forest in an STL map, but parents are done with pointers
struct Entry {
typename std::map<KEY, Entry>::iterator parent_;
size_t rank_;
Entry() {}
};
typedef typename std::map<KEY, Entry> Map;
typedef typename Map::iterator iterator;
mutable Map entries_;
typedef typename Map::iterator iterator;
mutable Map entries_;
/// Given key, find iterator to initial entry
iterator find__(const KEY& key) const {
static const Entry empty;
iterator it = entries_.find(key);
// if key does not exist, create and return itself
if (it == entries_.end()) {
it = entries_.insert(std::make_pair(key, empty)).first;
it->second.parent_ = it;
it->second.rank_ = 0;
}
return it;
}
/// Given key, find iterator to initial entry
iterator find__(const KEY& key) const {
static const Entry empty;
iterator it = entries_.find(key);
// if key does not exist, create and return itself
if (it == entries_.end()) {
it = entries_.insert(std::make_pair(key, empty)).first;
it->second.parent_ = it;
it->second.rank_ = 0;
}
return it;
}
/// Given iterator to initial entry, find the root Entry
iterator find_(const iterator& it) const {
// follow parent pointers until we reach set representative
iterator& parent = it->second.parent_;
if (parent != it)
parent = find_(parent); // not yet, recurse!
return parent;
}
/// Given iterator to initial entry, find the root Entry
iterator find_(const iterator& it) const {
// follow parent pointers until we reach set representative
iterator& parent = it->second.parent_;
if (parent != it)
parent = find_(parent); // not yet, recurse!
return parent;
}
/// Given key, find the root Entry
inline iterator find_(const KEY& key) const {
iterator initial = find__(key);
return find_(initial);
}
/// Given key, find the root Entry
inline iterator find_(const KEY& key) const {
iterator initial = find__(key);
return find_(initial);
}
public:
typedef std::set<KEY> Set;
typedef std::set<KEY> Set;
/// constructor
DSFMap() {
}
/// constructor
DSFMap() {
}
/// Given key, find the representative key for the set in which it lives
inline KEY find(const KEY& key) const {
iterator root = find_(key);
return root->first;
}
/// Given key, find the representative key for the set in which it lives
inline KEY find(const KEY& key) const {
iterator root = find_(key);
return root->first;
}
/// Merge two sets
void merge(const KEY& x, const KEY& y) {
/// Merge two sets
void merge(const KEY& x, const KEY& y) {
// straight from http://en.wikipedia.org/wiki/Disjoint-set_data_structure
iterator xRoot = find_(x);
iterator yRoot = find_(y);
if (xRoot == yRoot)
return;
// straight from http://en.wikipedia.org/wiki/Disjoint-set_data_structure
iterator xRoot = find_(x);
iterator yRoot = find_(y);
if (xRoot == yRoot)
return;
// Merge sets
if (xRoot->second.rank_ < yRoot->second.rank_)
xRoot->second.parent_ = yRoot;
else if (xRoot->second.rank_ > yRoot->second.rank_)
yRoot->second.parent_ = xRoot;
else {
yRoot->second.parent_ = xRoot;
xRoot->second.rank_ = xRoot->second.rank_ + 1;
}
}
// Merge sets
if (xRoot->second.rank_ < yRoot->second.rank_)
xRoot->second.parent_ = yRoot;
else if (xRoot->second.rank_ > yRoot->second.rank_)
yRoot->second.parent_ = xRoot;
else {
yRoot->second.parent_ = xRoot;
xRoot->second.rank_ = xRoot->second.rank_ + 1;
}
}
/// return all sets, i.e. a partition of all elements
std::map<KEY, Set> sets() const {
std::map<KEY, Set> sets;
iterator it = entries_.begin();
for (; it != entries_.end(); it++) {
iterator root = find_(it);
sets[root->first].insert(it->first);
}
return sets;
}
/// return all sets, i.e. a partition of all elements
std::map<KEY, Set> sets() const {
std::map<KEY, Set> sets;
iterator it = entries_.begin();
for (; it != entries_.end(); it++) {
iterator root = find_(it);
sets[root->first].insert(it->first);
}
return sets;
}
};

4
gtsam_unstable/discrete/tests/testLoopyBelief.cpp
View File

@ -1,8 +1,8 @@
/**
* @file testLoopyBelief.cpp
* @file testLoopyBelief.cpp
* @brief
* @author Duy-Nguyen Ta
* @date Oct 11, 2013
* @date Oct 11, 2013
*/
#include <gtsam/inference/VariableIndex.h>

906
gtsam_unstable/partition/FindSeparator-inl.h
View File

@ -26,539 +26,539 @@ extern "C" {
namespace gtsam { namespace partition {
typedef boost::shared_array<idx_t> sharedInts;
typedef boost::shared_array<idx_t> sharedInts;
/* ************************************************************************* */
/**
* Return the size of the separator and the partiion indices {part}
* Part [j] is 0, 1, or 2, depending on
* whether node j is in the left part of the graph, the right part, or the
* separator, respectively
*/
std::pair<int, sharedInts> separatorMetis(idx_t n, const sharedInts& xadj,
const sharedInts& adjncy, const sharedInts& adjwgt, bool verbose) {
/* ************************************************************************* */
/**
* Return the size of the separator and the partiion indices {part}
* Part [j] is 0, 1, or 2, depending on
* whether node j is in the left part of the graph, the right part, or the
* separator, respectively
*/
std::pair<int, sharedInts> separatorMetis(idx_t n, const sharedInts& xadj,
const sharedInts& adjncy, const sharedInts& adjwgt, bool verbose) {
// control parameters
idx_t vwgt[n]; // the weights of the vertices
idx_t options[METIS_NOPTIONS];
METIS_SetDefaultOptions(options); // use defaults
idx_t sepsize; // the size of the separator, output
sharedInts part_(new idx_t[n]); // the partition of each vertex, output
// control parameters
idx_t vwgt[n]; // the weights of the vertices
idx_t options[METIS_NOPTIONS];
METIS_SetDefaultOptions(options); // use defaults
idx_t sepsize; // the size of the separator, output
sharedInts part_(new idx_t[n]); // the partition of each vertex, output
// set uniform weights on the vertices
std::fill(vwgt, vwgt+n, 1);
// set uniform weights on the vertices
std::fill(vwgt, vwgt+n, 1);
// TODO: Fix at later time
//boost::timer::cpu_timer TOTALTmr;
if (verbose) {
printf("**********************************************************************\n");
printf("Graph Information ---------------------------------------------------\n");
printf(" #Vertices: %d, #Edges: %u\n", n, *(xadj.get()+n) / 2);
printf("\nND Partitioning... -------------------------------------------\n");
//TOTALTmr.start()
}
// TODO: Fix at later time
//boost::timer::cpu_timer TOTALTmr;
if (verbose) {
printf("**********************************************************************\n");
printf("Graph Information ---------------------------------------------------\n");
printf(" #Vertices: %d, #Edges: %u\n", n, *(xadj.get()+n) / 2);
printf("\nND Partitioning... -------------------------------------------\n");
//TOTALTmr.start()
}
// call metis parition routine
METIS_ComputeVertexSeparator(&n, xadj.get(), adjncy.get(),
// call metis parition routine
METIS_ComputeVertexSeparator(&n, xadj.get(), adjncy.get(),
vwgt, options, &sepsize, part_.get());
if (verbose) {
//boost::cpu_times const elapsed_times(timer.elapsed());
//printf("\nTiming Information --------------------------------------------------\n");
//printf(" Total: \t\t %7.3f\n", elapsed_times);
printf(" Sep size: \t\t %d\n", sepsize);
printf("**********************************************************************\n");
}
if (verbose) {
//boost::cpu_times const elapsed_times(timer.elapsed());
//printf("\nTiming Information --------------------------------------------------\n");
//printf(" Total: \t\t %7.3f\n", elapsed_times);
printf(" Sep size: \t\t %d\n", sepsize);
printf("**********************************************************************\n");
}
return std::make_pair(sepsize, part_);
}
return std::make_pair(sepsize, part_);
}
/* ************************************************************************* */
void modefied_EdgeComputeSeparator(idx_t *nvtxs, idx_t *xadj, idx_t *adjncy, idx_t *vwgt,
idx_t *adjwgt, idx_t *options, idx_t *edgecut, idx_t *part)
{
/* ************************************************************************* */
void modefied_EdgeComputeSeparator(idx_t *nvtxs, idx_t *xadj, idx_t *adjncy, idx_t *vwgt,
idx_t *adjwgt, idx_t *options, idx_t *edgecut, idx_t *part)
{
idx_t i, ncon;
graph_t *graph;
real_t *tpwgts2;
ctrl_t *ctrl;
ctrl = SetupCtrl(METIS_OP_OMETIS, options, 1, 3, NULL, NULL);
ctrl->iptype = METIS_IPTYPE_GROW;
//if () == NULL)
// return METIS_ERROR_INPUT;
graph_t *graph;
real_t *tpwgts2;
ctrl_t *ctrl;
ctrl = SetupCtrl(METIS_OP_OMETIS, options, 1, 3, NULL, NULL);
ctrl->iptype = METIS_IPTYPE_GROW;
//if () == NULL)
// return METIS_ERROR_INPUT;
InitRandom(ctrl->seed);
InitRandom(ctrl->seed);
graph = SetupGraph(ctrl, *nvtxs, 1, xadj, adjncy, vwgt, NULL, NULL);
graph = SetupGraph(ctrl, *nvtxs, 1, xadj, adjncy, vwgt, NULL, NULL);
AllocateWorkSpace(ctrl, graph);
AllocateWorkSpace(ctrl, graph);
ncon = graph->ncon;
ctrl->ncuts = 1;
ncon = graph->ncon;
ctrl->ncuts = 1;
/* determine the weights of the two partitions as a function of the weight of the
target partition weights */
/* determine the weights of the two partitions as a function of the weight of the
target partition weights */
tpwgts2 = rwspacemalloc(ctrl, 2*ncon);
for (i=0; i<ncon; i++) {
tpwgts2[i] = rsum((2>>1), ctrl->tpwgts+i, ncon);
tpwgts2[ncon+i] = 1.0 - tpwgts2[i];
}
/* perform the bisection */
*edgecut = MultilevelBisect(ctrl, graph, tpwgts2);
tpwgts2 = rwspacemalloc(ctrl, 2*ncon);
for (i=0; i<ncon; i++) {
tpwgts2[i] = rsum((2>>1), ctrl->tpwgts+i, ncon);
tpwgts2[ncon+i] = 1.0 - tpwgts2[i];
}
/* perform the bisection */
*edgecut = MultilevelBisect(ctrl, graph, tpwgts2);
// ConstructMinCoverSeparator(&ctrl, &graph, 1.05);
// *edgecut = graph->mincut;
// *sepsize = graph.pwgts[2];
icopy(*nvtxs, graph->where, part);
std::cout << "Finished bisection:" << *edgecut << std::endl;
FreeGraph(&graph);
// ConstructMinCoverSeparator(&ctrl, &graph, 1.05);
// *edgecut = graph->mincut;
// *sepsize = graph.pwgts[2];
icopy(*nvtxs, graph->where, part);
std::cout << "Finished bisection:" << *edgecut << std::endl;
FreeGraph(&graph);
FreeCtrl(&ctrl);
}
FreeCtrl(&ctrl);
}
/* ************************************************************************* */
/**
* Return the number of edge cuts and the partition indices {part}
* Part [j] is 0 or 1, depending on
* whether node j is in the left part of the graph or the right part respectively
*/
std::pair<int, sharedInts> edgeMetis(idx_t n, const sharedInts& xadj, const sharedInts& adjncy,
const sharedInts& adjwgt, bool verbose) {
/* ************************************************************************* */
/**
* Return the number of edge cuts and the partition indices {part}
* Part [j] is 0 or 1, depending on
* whether node j is in the left part of the graph or the right part respectively
*/
std::pair<int, sharedInts> edgeMetis(idx_t n, const sharedInts& xadj, const sharedInts& adjncy,
const sharedInts& adjwgt, bool verbose) {
// control parameters
idx_t vwgt[n]; // the weights of the vertices
idx_t options[METIS_NOPTIONS];
METIS_SetDefaultOptions(options); // use defaults
idx_t edgecut; // the number of edge cuts, output
sharedInts part_(new idx_t[n]); // the partition of each vertex, output
// control parameters
idx_t vwgt[n]; // the weights of the vertices
idx_t options[METIS_NOPTIONS];
METIS_SetDefaultOptions(options); // use defaults
idx_t edgecut; // the number of edge cuts, output
sharedInts part_(new idx_t[n]); // the partition of each vertex, output
// set uniform weights on the vertices
std::fill(vwgt, vwgt+n, 1);
// set uniform weights on the vertices
std::fill(vwgt, vwgt+n, 1);
//TODO: Fix later
//boost::timer TOTALTmr;
if (verbose) {
printf("**********************************************************************\n");
printf("Graph Information ---------------------------------------------------\n");
printf(" #Vertices: %d, #Edges: %u\n", n, *(xadj.get()+n) / 2);
printf("\nND Partitioning... -------------------------------------------\n");
//cleartimer(TOTALTmr);
//starttimer(TOTALTmr);
}
//TODO: Fix later
//boost::timer TOTALTmr;
if (verbose) {
printf("**********************************************************************\n");
printf("Graph Information ---------------------------------------------------\n");
printf(" #Vertices: %d, #Edges: %u\n", n, *(xadj.get()+n) / 2);
printf("\nND Partitioning... -------------------------------------------\n");
//cleartimer(TOTALTmr);
//starttimer(TOTALTmr);
}
//int wgtflag = 1; // only edge weights
//int numflag = 0; // c style numbering starting from 0
//int nparts = 2; // partition the graph to 2 submaps
modefied_EdgeComputeSeparator(&n, xadj.get(), adjncy.get(), vwgt, adjwgt.get(),
options, &edgecut, part_.get());
//int wgtflag = 1; // only edge weights
//int numflag = 0; // c style numbering starting from 0
//int nparts = 2; // partition the graph to 2 submaps
modefied_EdgeComputeSeparator(&n, xadj.get(), adjncy.get(), vwgt, adjwgt.get(),
options, &edgecut, part_.get());
if (verbose) {
//stoptimer(TOTALTmr);
printf("\nTiming Information --------------------------------------------------\n");
//printf(" Total: \t\t %7.3f\n", gettimer(TOTALTmr));
printf(" Edge cuts: \t\t %d\n", edgecut);
printf("**********************************************************************\n");
}
if (verbose) {
//stoptimer(TOTALTmr);
printf("\nTiming Information --------------------------------------------------\n");
//printf(" Total: \t\t %7.3f\n", gettimer(TOTALTmr));
printf(" Edge cuts: \t\t %d\n", edgecut);
printf("**********************************************************************\n");
}
return std::make_pair(edgecut, part_);
}
return std::make_pair(edgecut, part_);
}
/* ************************************************************************* */
/**
* Prepare the data structure {xadj} and {adjncy} required by metis
* xadj always has the size equal to the no. of the nodes plus 1
* adjncy always has the size equal to two times of the no. of the edges in the Metis graph
*/
template <class GenericGraph>
void prepareMetisGraph(const GenericGraph& graph, const std::vector<size_t>& keys, WorkSpace& workspace,
sharedInts* ptr_xadj, sharedInts* ptr_adjncy, sharedInts* ptr_adjwgt) {
/* ************************************************************************* */
/**
* Prepare the data structure {xadj} and {adjncy} required by metis
* xadj always has the size equal to the no. of the nodes plus 1
* adjncy always has the size equal to two times of the no. of the edges in the Metis graph
*/
template <class GenericGraph>
void prepareMetisGraph(const GenericGraph& graph, const std::vector<size_t>& keys, WorkSpace& workspace,
sharedInts* ptr_xadj, sharedInts* ptr_adjncy, sharedInts* ptr_adjwgt) {
typedef int Weight;
typedef std::vector<int> Weights;
typedef std::vector<int> Neighbors;
typedef std::pair<Neighbors, Weights> NeighborsInfo;
typedef int Weight;
typedef std::vector<int> Weights;
typedef std::vector<int> Neighbors;
typedef std::pair<Neighbors, Weights> NeighborsInfo;
// set up dictionary
std::vector<int>& dictionary = workspace.dictionary;
workspace.prepareDictionary(keys);
// set up dictionary
std::vector<int>& dictionary = workspace.dictionary;
workspace.prepareDictionary(keys);
// prepare for {adjacencyMap}, a pair of neighbor indices and the correponding edge weights
int numNodes = keys.size();
int numEdges = 0;
std::vector<NeighborsInfo> adjacencyMap;
adjacencyMap.resize(numNodes);
std::cout << "Number of nodes: " << adjacencyMap.size() << std::endl;
int index1, index2;
// prepare for {adjacencyMap}, a pair of neighbor indices and the correponding edge weights
int numNodes = keys.size();
int numEdges = 0;
std::vector<NeighborsInfo> adjacencyMap;
adjacencyMap.resize(numNodes);
std::cout << "Number of nodes: " << adjacencyMap.size() << std::endl;
int index1, index2;
BOOST_FOREACH(const typename GenericGraph::value_type& factor, graph){
index1 = dictionary[factor->key1.index];
index2 = dictionary[factor->key2.index];
std::cout << "index1: " << index1 << std::endl;
std::cout << "index2: " << index2 << std::endl;
// if both nodes are in the current graph, i.e. not a joint factor between frontal and separator
if (index1 >= 0 && index2 >= 0) {
std::pair<Neighbors, Weights>& adjacencyMap1 = adjacencyMap[index1];
std::pair<Neighbors, Weights>& adjacencyMap2 = adjacencyMap[index2];
try{
adjacencyMap1.first.push_back(index2);
adjacencyMap1.second.push_back(factor->weight);
adjacencyMap2.first.push_back(index1);
adjacencyMap2.second.push_back(factor->weight);
}catch(std::exception& e){
std::cout << e.what() << std::endl;
}
numEdges++;
}
}
BOOST_FOREACH(const typename GenericGraph::value_type& factor, graph){
index1 = dictionary[factor->key1.index];
index2 = dictionary[factor->key2.index];
std::cout << "index1: " << index1 << std::endl;
std::cout << "index2: " << index2 << std::endl;
// if both nodes are in the current graph, i.e. not a joint factor between frontal and separator
if (index1 >= 0 && index2 >= 0) {
std::pair<Neighbors, Weights>& adjacencyMap1 = adjacencyMap[index1];
std::pair<Neighbors, Weights>& adjacencyMap2 = adjacencyMap[index2];
try{
adjacencyMap1.first.push_back(index2);
adjacencyMap1.second.push_back(factor->weight);
adjacencyMap2.first.push_back(index1);
adjacencyMap2.second.push_back(factor->weight);
}catch(std::exception& e){
std::cout << e.what() << std::endl;
}
numEdges++;
}
}
// prepare for {xadj}, {adjncy}, and {adjwgt}
*ptr_xadj = sharedInts(new idx_t[numNodes+1]);
*ptr_adjncy = sharedInts(new idx_t[numEdges*2]);
*ptr_adjwgt = sharedInts(new idx_t[numEdges*2]);
sharedInts& xadj = *ptr_xadj;
sharedInts& adjncy = *ptr_adjncy;
sharedInts& adjwgt = *ptr_adjwgt;
int ind_xadj = 0, ind_adjncy = 0;
BOOST_FOREACH(const NeighborsInfo& info, adjacencyMap) {
*(xadj.get() + ind_xadj) = ind_adjncy;
std::copy(info.first .begin(), info.first .end(), adjncy.get() + ind_adjncy);
std::copy(info.second.begin(), info.second.end(), adjwgt.get() + ind_adjncy);
assert(info.first.size() == info.second.size());
ind_adjncy += info.first.size();
ind_xadj ++;
}
if (ind_xadj != numNodes) throw std::runtime_error("prepareMetisGraph_: ind_xadj != numNodes");
*(xadj.get() + ind_xadj) = ind_adjncy;
}
// prepare for {xadj}, {adjncy}, and {adjwgt}
*ptr_xadj = sharedInts(new idx_t[numNodes+1]);
*ptr_adjncy = sharedInts(new idx_t[numEdges*2]);
*ptr_adjwgt = sharedInts(new idx_t[numEdges*2]);
sharedInts& xadj = *ptr_xadj;
sharedInts& adjncy = *ptr_adjncy;
sharedInts& adjwgt = *ptr_adjwgt;
int ind_xadj = 0, ind_adjncy = 0;
BOOST_FOREACH(const NeighborsInfo& info, adjacencyMap) {
*(xadj.get() + ind_xadj) = ind_adjncy;
std::copy(info.first .begin(), info.first .end(), adjncy.get() + ind_adjncy);
std::copy(info.second.begin(), info.second.end(), adjwgt.get() + ind_adjncy);
assert(info.first.size() == info.second.size());
ind_adjncy += info.first.size();
ind_xadj ++;
}
if (ind_xadj != numNodes) throw std::runtime_error("prepareMetisGraph_: ind_xadj != numNodes");
*(xadj.get() + ind_xadj) = ind_adjncy;
}
/* ************************************************************************* */
template<class GenericGraph>
boost::optional<MetisResult> separatorPartitionByMetis(const GenericGraph& graph,
const std::vector<size_t>& keys, WorkSpace& workspace, bool verbose) {
// create a metis graph
size_t numKeys = keys.size();
if (verbose)
std::cout << graph.size() << " factors,\t" << numKeys << " nodes;\t" << std::endl;
/* ************************************************************************* */
template<class GenericGraph>
boost::optional<MetisResult> separatorPartitionByMetis(const GenericGraph& graph,
const std::vector<size_t>& keys, WorkSpace& workspace, bool verbose) {
// create a metis graph
size_t numKeys = keys.size();
if (verbose)
std::cout << graph.size() << " factors,\t" << numKeys << " nodes;\t" << std::endl;
sharedInts xadj, adjncy, adjwgt;
sharedInts xadj, adjncy, adjwgt;
prepareMetisGraph<GenericGraph>(graph, keys, workspace, &xadj, &adjncy, &adjwgt);
prepareMetisGraph<GenericGraph>(graph, keys, workspace, &xadj, &adjncy, &adjwgt);
// run ND on the graph
size_t sepsize;
sharedInts part;
boost::tie(sepsize, part) = separatorMetis(numKeys, xadj, adjncy, adjwgt, verbose);
if (!sepsize) return boost::optional<MetisResult>();
// run ND on the graph
size_t sepsize;
sharedInts part;
boost::tie(sepsize, part) = separatorMetis(numKeys, xadj, adjncy, adjwgt, verbose);
if (!sepsize) return boost::optional<MetisResult>();
// convert the 0-1-2 from Metis to 1-2-0, so that the separator is 0, as later
// we will have more submaps
MetisResult result;
result.C.reserve(sepsize);
result.A.reserve(numKeys - sepsize);
result.B.reserve(numKeys - sepsize);
int* ptr_part = part.get();
std::vector<size_t>::const_iterator itKey = keys.begin();
std::vector<size_t>::const_iterator itKeyLast = keys.end();
while(itKey != itKeyLast) {
switch(*(ptr_part++)) {
case 0: result.A.push_back(*(itKey++)); break;
case 1: result.B.push_back(*(itKey++)); break;
case 2: result.C.push_back(*(itKey++)); break;
default: throw std::runtime_error("separatorPartitionByMetis: invalid results from Metis ND!");
}
}
// convert the 0-1-2 from Metis to 1-2-0, so that the separator is 0, as later
// we will have more submaps
MetisResult result;
result.C.reserve(sepsize);
result.A.reserve(numKeys - sepsize);
result.B.reserve(numKeys - sepsize);
int* ptr_part = part.get();
std::vector<size_t>::const_iterator itKey = keys.begin();
std::vector<size_t>::const_iterator itKeyLast = keys.end();
while(itKey != itKeyLast) {
switch(*(ptr_part++)) {
case 0: result.A.push_back(*(itKey++)); break;
case 1: result.B.push_back(*(itKey++)); break;
case 2: result.C.push_back(*(itKey++)); break;
default: throw std::runtime_error("separatorPartitionByMetis: invalid results from Metis ND!");
}
}
if (verbose) {
std::cout << "total key: " << keys.size()
<< " result(A,B,C) = " << result.A.size() << ", " << result.B.size() << ", "
<< result.C.size() << "; sepsize from Metis = " << sepsize << std::endl;
//throw runtime_error("separatorPartitionByMetis:stop for debug");
}
if (verbose) {
std::cout << "total key: " << keys.size()
<< " result(A,B,C) = " << result.A.size() << ", " << result.B.size() << ", "
<< result.C.size() << "; sepsize from Metis = " << sepsize << std::endl;
//throw runtime_error("separatorPartitionByMetis:stop for debug");
}
if(result.C.size() != sepsize) {
std::cout << "total key: " << keys.size()
<< " result(A,B,C) = " << result.A.size() << ", " << result.B.size() << ", " << result.C.size()
<< "; sepsize from Metis = " << sepsize << std::endl;
throw std::runtime_error("separatorPartitionByMetis: invalid sepsize from Metis ND!");
}
if(result.C.size() != sepsize) {
std::cout << "total key: " << keys.size()
<< " result(A,B,C) = " << result.A.size() << ", " << result.B.size() << ", " << result.C.size()
<< "; sepsize from Metis = " << sepsize << std::endl;
throw std::runtime_error("separatorPartitionByMetis: invalid sepsize from Metis ND!");
}
return boost::make_optional<MetisResult >(result);
}
return boost::make_optional<MetisResult >(result);
}
/* *************************************************************************/
template<class GenericGraph>
boost::optional<MetisResult> edgePartitionByMetis(const GenericGraph& graph,
const std::vector<size_t>& keys, WorkSpace& workspace, bool verbose) {
/* *************************************************************************/
template<class GenericGraph>
boost::optional<MetisResult> edgePartitionByMetis(const GenericGraph& graph,
const std::vector<size_t>& keys, WorkSpace& workspace, bool verbose) {
// a small hack for handling the camera1-camera2 case used in the unit tests
if (graph.size() == 1 && keys.size() == 2) {
MetisResult result;
result.A.push_back(keys.front());
result.B.push_back(keys.back());
return result;
}
// a small hack for handling the camera1-camera2 case used in the unit tests
if (graph.size() == 1 && keys.size() == 2) {
MetisResult result;
result.A.push_back(keys.front());
result.B.push_back(keys.back());
return result;
}
// create a metis graph
size_t numKeys = keys.size();
if (verbose) std::cout << graph.size() << " factors,\t" << numKeys << " nodes;\t" << std::endl;
sharedInts xadj, adjncy, adjwgt;
prepareMetisGraph<GenericGraph>(graph, keys, workspace, &xadj, &adjncy, &adjwgt);
// create a metis graph
size_t numKeys = keys.size();
if (verbose) std::cout << graph.size() << " factors,\t" << numKeys << " nodes;\t" << std::endl;
sharedInts xadj, adjncy, adjwgt;
prepareMetisGraph<GenericGraph>(graph, keys, workspace, &xadj, &adjncy, &adjwgt);
// run metis on the graph
int edgecut;
sharedInts part;
boost::tie(edgecut, part) = edgeMetis(numKeys, xadj, adjncy, adjwgt, verbose);
// run metis on the graph
int edgecut;
sharedInts part;
boost::tie(edgecut, part) = edgeMetis(numKeys, xadj, adjncy, adjwgt, verbose);
// convert the 0-1-2 from Metis to 1-2-0, so that the separator is 0, as later we will have more submaps
MetisResult result;
result.A.reserve(numKeys);
result.B.reserve(numKeys);
int* ptr_part = part.get();
std::vector<size_t>::const_iterator itKey = keys.begin();
std::vector<size_t>::const_iterator itKeyLast = keys.end();
while(itKey != itKeyLast) {
if (*ptr_part != 0 && *ptr_part != 1)
std::cout << *ptr_part << "!!!" << std::endl;
switch(*(ptr_part++)) {
case 0: result.A.push_back(*(itKey++)); break;
case 1: result.B.push_back(*(itKey++)); break;
default: throw std::runtime_error("edgePartitionByMetis: invalid results from Metis ND!");
}
}
// convert the 0-1-2 from Metis to 1-2-0, so that the separator is 0, as later we will have more submaps
MetisResult result;
result.A.reserve(numKeys);
result.B.reserve(numKeys);
int* ptr_part = part.get();
std::vector<size_t>::const_iterator itKey = keys.begin();
std::vector<size_t>::const_iterator itKeyLast = keys.end();
while(itKey != itKeyLast) {
if (*ptr_part != 0 && *ptr_part != 1)
std::cout << *ptr_part << "!!!" << std::endl;
switch(*(ptr_part++)) {
case 0: result.A.push_back(*(itKey++)); break;
case 1: result.B.push_back(*(itKey++)); break;
default: throw std::runtime_error("edgePartitionByMetis: invalid results from Metis ND!");
}
}
if (verbose) {
std::cout << "the size of two submaps in the reduced graph: " << result.A.size()
<< " " << result.B.size() << std::endl;
int edgeCut = 0;
if (verbose) {
std::cout << "the size of two submaps in the reduced graph: " << result.A.size()
<< " " << result.B.size() << std::endl;
int edgeCut = 0;
BOOST_FOREACH(const typename GenericGraph::value_type& factor, graph){
int key1 = factor->key1.index;
int key2 = factor->key2.index;
// print keys and their subgraph assignment
std::cout << key1;
if (std::find(result.A.begin(), result.A.end(), key1) != result.A.end()) std::cout <<"A ";
if (std::find(result.B.begin(), result.B.end(), key1) != result.B.end()) std::cout <<"B ";
BOOST_FOREACH(const typename GenericGraph::value_type& factor, graph){
int key1 = factor->key1.index;
int key2 = factor->key2.index;
// print keys and their subgraph assignment
std::cout << key1;
if (std::find(result.A.begin(), result.A.end(), key1) != result.A.end()) std::cout <<"A ";
if (std::find(result.B.begin(), result.B.end(), key1) != result.B.end()) std::cout <<"B ";
std::cout << key2;
if (std::find(result.A.begin(), result.A.end(), key2) != result.A.end()) std::cout <<"A ";
if (std::find(result.B.begin(), result.B.end(), key2) != result.B.end()) std::cout <<"B ";
std::cout << key2;
if (std::find(result.A.begin(), result.A.end(), key2) != result.A.end()) std::cout <<"A ";
if (std::find(result.B.begin(), result.B.end(), key2) != result.B.end()) std::cout <<"B ";
std::cout << "weight " << factor->weight;;
// find vertices that were assigned to sets A & B. Their edge will be cut
if ((std::find(result.A.begin(), result.A.end(), key1) != result.A.end() &&
std::find(result.B.begin(), result.B.end(), key2) != result.B.end()) ||
(std::find(result.B.begin(), result.B.end(), key1) != result.B.end() &&
std::find(result.A.begin(), result.A.end(), key2) != result.A.end())){
edgeCut ++;
std::cout << " CUT ";
}
std::cout << std::endl;
}
std::cout << "edgeCut: " << edgeCut << std::endl;
}
// find vertices that were assigned to sets A & B. Their edge will be cut
if ((std::find(result.A.begin(), result.A.end(), key1) != result.A.end() &&
std::find(result.B.begin(), result.B.end(), key2) != result.B.end()) ||
(std::find(result.B.begin(), result.B.end(), key1) != result.B.end() &&
std::find(result.A.begin(), result.A.end(), key2) != result.A.end())){
edgeCut ++;
std::cout << " CUT ";
}
std::cout << std::endl;
}
std::cout << "edgeCut: " << edgeCut << std::endl;
}
return boost::make_optional<MetisResult >(result);
}
return boost::make_optional<MetisResult >(result);
}
/* ************************************************************************* */
bool isLargerIsland(const std::vector<size_t>& island1, const std::vector<size_t>& island2) {
return island1.size() > island2.size();
}
/* ************************************************************************* */
bool isLargerIsland(const std::vector<size_t>& island1, const std::vector<size_t>& island2) {
return island1.size() > island2.size();
}
/* ************************************************************************* */
// debug functions
void printIsland(const std::vector<size_t>& island) {
std::cout << "island: ";
BOOST_FOREACH(const size_t key, island)
std::cout << key << " ";
std::cout << std::endl;
}
/* ************************************************************************* */
// debug functions
void printIsland(const std::vector<size_t>& island) {
std::cout << "island: ";
BOOST_FOREACH(const size_t key, island)
std::cout << key << " ";
std::cout << std::endl;
}
void printIslands(const std::list<std::vector<size_t> >& islands) {
BOOST_FOREACH(const std::vector<std::size_t>& island, islands)
printIsland(island);
}
void printIslands(const std::list<std::vector<size_t> >& islands) {
BOOST_FOREACH(const std::vector<std::size_t>& island, islands)
printIsland(island);
}
void printNumCamerasLandmarks(const std::vector<size_t>& keys, const std::vector<Symbol>& int2symbol) {
int numCamera = 0, numLandmark = 0;
BOOST_FOREACH(const size_t key, keys)
if (int2symbol[key].chr() == 'x')
numCamera++;
else
numLandmark++;
std::cout << "numCamera: " << numCamera << " numLandmark: " << numLandmark << std::endl;
}
void printNumCamerasLandmarks(const std::vector<size_t>& keys, const std::vector<Symbol>& int2symbol) {
int numCamera = 0, numLandmark = 0;
BOOST_FOREACH(const size_t key, keys)
if (int2symbol[key].chr() == 'x')
numCamera++;
else
numLandmark++;
std::cout << "numCamera: " << numCamera << " numLandmark: " << numLandmark << std::endl;
}
/* ************************************************************************* */
template<class GenericGraph>
void addLandmarkToPartitionResult(const GenericGraph& graph, const std::vector<size_t>& landmarkKeys,
MetisResult& partitionResult, WorkSpace& workspace) {
/* ************************************************************************* */
template<class GenericGraph>
void addLandmarkToPartitionResult(const GenericGraph& graph, const std::vector<size_t>& landmarkKeys,
MetisResult& partitionResult, WorkSpace& workspace) {
// set up cameras in the dictionary
std::vector<size_t>& A = partitionResult.A;
std::vector<size_t>& B = partitionResult.B;
std::vector<size_t>& C = partitionResult.C;
std::vector<int>& dictionary = workspace.dictionary;
std::fill(dictionary.begin(), dictionary.end(), -1);
BOOST_FOREACH(const size_t a, A)
dictionary[a] = 1;
BOOST_FOREACH(const size_t b, B)
dictionary[b] = 2;
if (!C.empty())
throw std::runtime_error("addLandmarkToPartitionResult: C is not empty");
// set up cameras in the dictionary
std::vector<size_t>& A = partitionResult.A;
std::vector<size_t>& B = partitionResult.B;
std::vector<size_t>& C = partitionResult.C;
std::vector<int>& dictionary = workspace.dictionary;
std::fill(dictionary.begin(), dictionary.end(), -1);
BOOST_FOREACH(const size_t a, A)
dictionary[a] = 1;
BOOST_FOREACH(const size_t b, B)
dictionary[b] = 2;
if (!C.empty())
throw std::runtime_error("addLandmarkToPartitionResult: C is not empty");
// set up landmarks
size_t i,j;
BOOST_FOREACH(const typename GenericGraph::value_type& factor, graph) {
i = factor->key1.index;
j = factor->key2.index;
if (dictionary[j] == 0) // if the landmark is already in the separator, continue
continue;
else if (dictionary[j] == -1)
dictionary[j] = dictionary[i];
else {
if (dictionary[j] != dictionary[i])
dictionary[j] = 0;
}
// if (j == 67980)
// std::cout << "dictionary[67980]" << dictionary[j] << std::endl;
}
// set up landmarks
size_t i,j;
BOOST_FOREACH(const typename GenericGraph::value_type& factor, graph) {
i = factor->key1.index;
j = factor->key2.index;
if (dictionary[j] == 0) // if the landmark is already in the separator, continue
continue;
else if (dictionary[j] == -1)
dictionary[j] = dictionary[i];
else {
if (dictionary[j] != dictionary[i])
dictionary[j] = 0;
}
// if (j == 67980)
// std::cout << "dictionary[67980]" << dictionary[j] << std::endl;
}
BOOST_FOREACH(const size_t j, landmarkKeys) {
switch(dictionary[j]) {
case 0: C.push_back(j); break;
case 1: A.push_back(j); break;
case 2: B.push_back(j); break;
default: std::cout << j << ": " << dictionary[j] << std::endl;
throw std::runtime_error("addLandmarkToPartitionResult: wrong status for landmark");
}
}
}
BOOST_FOREACH(const size_t j, landmarkKeys) {
switch(dictionary[j]) {
case 0: C.push_back(j); break;
case 1: A.push_back(j); break;
case 2: B.push_back(j); break;
default: std::cout << j << ": " << dictionary[j] << std::endl;
throw std::runtime_error("addLandmarkToPartitionResult: wrong status for landmark");
}
}
}
#define REDUCE_CAMERA_GRAPH
/* ************************************************************************* */
template<class GenericGraph>
boost::optional<MetisResult> findPartitoning(const GenericGraph& graph, const std::vector<size_t>& keys,
WorkSpace& workspace, bool verbose,
const boost::optional<std::vector<Symbol> >& int2symbol, const bool reduceGraph) {
boost::optional<MetisResult> result;
GenericGraph reducedGraph;
std::vector<size_t> keyToPartition;
std::vector<size_t> cameraKeys, landmarkKeys;
if (reduceGraph) {
if (!int2symbol.is_initialized())
throw std::invalid_argument("findSeparator: int2symbol must be valid!");
/* ************************************************************************* */
template<class GenericGraph>
boost::optional<MetisResult> findPartitoning(const GenericGraph& graph, const std::vector<size_t>& keys,
WorkSpace& workspace, bool verbose,
const boost::optional<std::vector<Symbol> >& int2symbol, const bool reduceGraph) {
boost::optional<MetisResult> result;
GenericGraph reducedGraph;
std::vector<size_t> keyToPartition;
std::vector<size_t> cameraKeys, landmarkKeys;
if (reduceGraph) {
if (!int2symbol.is_initialized())
throw std::invalid_argument("findSeparator: int2symbol must be valid!");
// find out all the landmark keys, which are to be eliminated
cameraKeys.reserve(keys.size());
landmarkKeys.reserve(keys.size());
BOOST_FOREACH(const size_t key, keys) {
if((*int2symbol)[key].chr() == 'x')
cameraKeys.push_back(key);
else
landmarkKeys.push_back(key);
}
// find out all the landmark keys, which are to be eliminated
cameraKeys.reserve(keys.size());
landmarkKeys.reserve(keys.size());
BOOST_FOREACH(const size_t key, keys) {
if((*int2symbol)[key].chr() == 'x')
cameraKeys.push_back(key);
else
landmarkKeys.push_back(key);
}
keyToPartition = cameraKeys;
workspace.prepareDictionary(keyToPartition);
const std::vector<int>& dictionary = workspace.dictionary;
reduceGenericGraph(graph, cameraKeys, landmarkKeys, dictionary, reducedGraph);
std::cout << "original graph: V" << keys.size() << ", E" << graph.size()
<< " --> reduced graph: V" << cameraKeys.size() << ", E" << reducedGraph.size() << std::endl;
result = edgePartitionByMetis(reducedGraph, keyToPartition, workspace, verbose);
} else // call Metis to partition the graph to A, B, C
result = separatorPartitionByMetis(graph, keys, workspace, verbose);
keyToPartition = cameraKeys;
workspace.prepareDictionary(keyToPartition);
const std::vector<int>& dictionary = workspace.dictionary;
reduceGenericGraph(graph, cameraKeys, landmarkKeys, dictionary, reducedGraph);
std::cout << "original graph: V" << keys.size() << ", E" << graph.size()
<< " --> reduced graph: V" << cameraKeys.size() << ", E" << reducedGraph.size() << std::endl;
result = edgePartitionByMetis(reducedGraph, keyToPartition, workspace, verbose);
} else // call Metis to partition the graph to A, B, C
result = separatorPartitionByMetis(graph, keys, workspace, verbose);
if (!result.is_initialized()) {
std::cout << "metis failed!" << std::endl;
return 0;
}
if (!result.is_initialized()) {
std::cout << "metis failed!" << std::endl;
return 0;
}
if (reduceGraph) {
addLandmarkToPartitionResult(graph, landmarkKeys, *result, workspace);
std::cout << "the separator size: " << result->C.size() << " landmarks" << std::endl;
}
if (reduceGraph) {
addLandmarkToPartitionResult(graph, landmarkKeys, *result, workspace);
std::cout << "the separator size: " << result->C.size() << " landmarks" << std::endl;
}
return result;
}
return result;
}
/* ************************************************************************* */
template<class GenericGraph>
int findSeparator(const GenericGraph& graph, const std::vector<size_t>& keys,
const int minNodesPerMap, WorkSpace& workspace, bool verbose,
const boost::optional<std::vector<Symbol> >& int2symbol, const bool reduceGraph,
const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark) {
/* ************************************************************************* */
template<class GenericGraph>
int findSeparator(const GenericGraph& graph, const std::vector<size_t>& keys,
const int minNodesPerMap, WorkSpace& workspace, bool verbose,
const boost::optional<std::vector<Symbol> >& int2symbol, const bool reduceGraph,
const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark) {
boost::optional<MetisResult> result = findPartitoning(graph, keys, workspace,
verbose, int2symbol, reduceGraph);
boost::optional<MetisResult> result = findPartitoning(graph, keys, workspace,
verbose, int2symbol, reduceGraph);
// find the island in A and B, and make them separated submaps
typedef std::vector<size_t> Island;
std::list<Island> islands;
// find the island in A and B, and make them separated submaps
typedef std::vector<size_t> Island;
std::list<Island> islands;
std::list<Island> islands_in_A = findIslands(graph, result->A, workspace,
minNrConstraintsPerCamera, minNrConstraintsPerLandmark);
std::list<Island> islands_in_A = findIslands(graph, result->A, workspace,
minNrConstraintsPerCamera, minNrConstraintsPerLandmark);
std::list<Island> islands_in_B = findIslands(graph, result->B, workspace,
minNrConstraintsPerCamera, minNrConstraintsPerLandmark);
std::list<Island> islands_in_B = findIslands(graph, result->B, workspace,
minNrConstraintsPerCamera, minNrConstraintsPerLandmark);
islands.insert(islands.end(), islands_in_A.begin(), islands_in_A.end());
islands.insert(islands.end(), islands_in_B.begin(), islands_in_B.end());
islands.sort(isLargerIsland);
size_t numIsland0 = islands.size();
islands.insert(islands.end(), islands_in_A.begin(), islands_in_A.end());
islands.insert(islands.end(), islands_in_B.begin(), islands_in_B.end());
islands.sort(isLargerIsland);
size_t numIsland0 = islands.size();
#ifdef NDEBUG
// verbose = true;
// if (!int2symbol) throw std::invalid_argument("findSeparator: int2symbol is not set!");
// std::cout << "sep size: " << result->C.size() << "; ";
// printNumCamerasLandmarks(result->C, *int2symbol);
// std::cout << "no. of island: " << islands.size() << "; ";
// std::cout << "island size: ";
// BOOST_FOREACH(const Island& island, islands)
// std::cout << island.size() << " ";
// std::cout << std::endl;
// verbose = true;
// if (!int2symbol) throw std::invalid_argument("findSeparator: int2symbol is not set!");
// std::cout << "sep size: " << result->C.size() << "; ";
// printNumCamerasLandmarks(result->C, *int2symbol);
// std::cout << "no. of island: " << islands.size() << "; ";
// std::cout << "island size: ";
// BOOST_FOREACH(const Island& island, islands)
// std::cout << island.size() << " ";
// std::cout << std::endl;
// BOOST_FOREACH(const Island& island, islands) {
// printNumCamerasLandmarks(island, int2symbol);
// }
// BOOST_FOREACH(const Island& island, islands) {
// printNumCamerasLandmarks(island, int2symbol);
// }
#endif
// absorb small components into the separator
size_t oldSize = islands.size();
while(true) {
if (islands.size() < 2) {
std::cout << "numIsland: " << numIsland0 << std::endl;
throw std::runtime_error("findSeparator: found fewer than 2 submaps!");
}
// absorb small components into the separator
size_t oldSize = islands.size();
while(true) {
if (islands.size() < 2) {
std::cout << "numIsland: " << numIsland0 << std::endl;
throw std::runtime_error("findSeparator: found fewer than 2 submaps!");
}
std::list<Island>::reference island = islands.back();
if ((int)island.size() >= minNodesPerMap) break;
result->C.insert(result->C.end(), island.begin(), island.end());
islands.pop_back();
}
if (islands.size() != oldSize){
if (verbose) std::cout << oldSize << "-" << oldSize - islands.size() << " submap(s);\t" << std::endl;
}
else{
if (verbose) std::cout << oldSize << " submap(s);\t" << std::endl;
}
std::list<Island>::reference island = islands.back();
if ((int)island.size() >= minNodesPerMap) break;
result->C.insert(result->C.end(), island.begin(), island.end());
islands.pop_back();
}
if (islands.size() != oldSize){
if (verbose) std::cout << oldSize << "-" << oldSize - islands.size() << " submap(s);\t" << std::endl;
}
else{
if (verbose) std::cout << oldSize << " submap(s);\t" << std::endl;
}
// generate the node map
std::vector<int>& partitionTable = workspace.partitionTable;
std::fill(partitionTable.begin(), partitionTable.end(), -1);
BOOST_FOREACH(const size_t key, result->C)
partitionTable[key] = 0;
int idx = 0;
BOOST_FOREACH(const Island& island, islands) {
idx++;
BOOST_FOREACH(const size_t key, island) {
partitionTable[key] = idx;
}
}
// generate the node map
std::vector<int>& partitionTable = workspace.partitionTable;
std::fill(partitionTable.begin(), partitionTable.end(), -1);
BOOST_FOREACH(const size_t key, result->C)
partitionTable[key] = 0;
int idx = 0;
BOOST_FOREACH(const Island& island, islands) {
idx++;
BOOST_FOREACH(const size_t key, island) {
partitionTable[key] = idx;
}
}
return islands.size();
}
return islands.size();
}
}} //namespace

42
gtsam_unstable/partition/FindSeparator.h
View File

@ -16,29 +16,29 @@
namespace gtsam { namespace partition {
// typedef std::map<size_t, size_t> PartitionTable; // from the key to the partition: 0 - separator, > 1: submap id
// typedef std::map<size_t, size_t> PartitionTable; // from the key to the partition: 0 - separator, > 1: submap id
/** the metis Nest dissection result */
struct MetisResult {
std::vector<size_t> A, B; // frontals
std::vector<size_t> C; // separator
};
/** the metis Nest dissection result */
struct MetisResult {
std::vector<size_t> A, B; // frontals
std::vector<size_t> C; // separator
};
/**
* use Metis library to partition, return the size of separator and the optional partition table
* the size of dictionary mush be equal to the number of variables in the original graph (the largest one)
*/
template<class GenericGraph>
boost::optional<MetisResult> separatorPartitionByMetis(const GenericGraph& graph, const std::vector<size_t>& keys,
WorkSpace& workspace, bool verbose);
/**
* use Metis library to partition, return the size of separator and the optional partition table
* the size of dictionary mush be equal to the number of variables in the original graph (the largest one)
*/
template<class GenericGraph>
boost::optional<MetisResult> separatorPartitionByMetis(const GenericGraph& graph, const std::vector<size_t>& keys,
WorkSpace& workspace, bool verbose);
/**
* return the number of submaps and the parition table of the partitioned graph (**stored in workspace.partitionTable**).
* return 0 if failed Note that the original output of Metis is 0,1 for submap, and 2 for the separator.
*/
template<class GenericGraph>
int findSeparator(const GenericGraph& graph, const std::vector<size_t>& keys,
const int minNodesPerMap, WorkSpace& workspace, bool verbose, const boost::optional<std::vector<Symbol> >& int2symbol,
const bool reduceGraph, const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark);
/**
* return the number of submaps and the parition table of the partitioned graph (**stored in workspace.partitionTable**).
* return 0 if failed Note that the original output of Metis is 0,1 for submap, and 2 for the separator.
*/
template<class GenericGraph>
int findSeparator(const GenericGraph& graph, const std::vector<size_t>& keys,
const int minNodesPerMap, WorkSpace& workspace, bool verbose, const boost::optional<std::vector<Symbol> >& int2symbol,
const bool reduceGraph, const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark);
}} //namespace

792
gtsam_unstable/partition/GenericGraph.cpp
View File

@ -19,459 +19,459 @@ using namespace std;
namespace gtsam { namespace partition {
/**
* Note: Need to be able to handle a graph with factors that involve variables not in the given {keys}
*/
list<vector<size_t> > findIslands(const GenericGraph2D& graph, const vector<size_t>& keys, WorkSpace& workspace,
const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark)
{
typedef pair<int, int> IntPair;
typedef list<sharedGenericFactor2D> FactorList;
typedef map<IntPair, FactorList::iterator> Connections;
/**
* Note: Need to be able to handle a graph with factors that involve variables not in the given {keys}
*/
list<vector<size_t> > findIslands(const GenericGraph2D& graph, const vector<size_t>& keys, WorkSpace& workspace,
const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark)
{
typedef pair<int, int> IntPair;
typedef list<sharedGenericFactor2D> FactorList;
typedef map<IntPair, FactorList::iterator> Connections;
// create disjoin set forest
DSFVector dsf(workspace.dsf, keys);
// create disjoin set forest
DSFVector dsf(workspace.dsf, keys);
FactorList factors(graph.begin(), graph.end());
size_t nrFactors = factors.size();
FactorList::iterator itEnd;
workspace.prepareDictionary(keys);
while (nrFactors) {
Connections connections;
bool succeed = false;
itEnd = factors.end();
list<FactorList::iterator> toErase;
for (FactorList::iterator itFactor=factors.begin(); itFactor!=itEnd; itFactor++) {
FactorList factors(graph.begin(), graph.end());
size_t nrFactors = factors.size();
FactorList::iterator itEnd;
workspace.prepareDictionary(keys);
while (nrFactors) {
Connections connections;
bool succeed = false;
itEnd = factors.end();
list<FactorList::iterator> toErase;
for (FactorList::iterator itFactor=factors.begin(); itFactor!=itEnd; itFactor++) {
// remove invalid factors
GenericNode2D key1 = (*itFactor)->key1, key2 = (*itFactor)->key2;
if (workspace.dictionary[key1.index]==-1 || workspace.dictionary[key2.index]==-1) {
toErase.push_back(itFactor); nrFactors--; continue;
}
// remove invalid factors
GenericNode2D key1 = (*itFactor)->key1, key2 = (*itFactor)->key2;
if (workspace.dictionary[key1.index]==-1 || workspace.dictionary[key2.index]==-1) {
toErase.push_back(itFactor); nrFactors--; continue;
}
size_t label1 = dsf.findSet(key1.index);
size_t label2 = dsf.findSet(key2.index);
if (label1 == label2) { toErase.push_back(itFactor); nrFactors--; continue; }
size_t label1 = dsf.findSet(key1.index);
size_t label2 = dsf.findSet(key2.index);
if (label1 == label2) { toErase.push_back(itFactor); nrFactors--; continue; }
// merge two trees if the connection is strong enough, otherwise cache it
// an odometry factor always merges two islands
if (key1.type == NODE_POSE_2D && key2.type == NODE_POSE_2D) {
toErase.push_back(itFactor); nrFactors--;
dsf.makeUnionInPlace(label1, label2);
succeed = true;
break;
}
// merge two trees if the connection is strong enough, otherwise cache it
// an odometry factor always merges two islands
if (key1.type == NODE_POSE_2D && key2.type == NODE_POSE_2D) {
toErase.push_back(itFactor); nrFactors--;
dsf.makeUnionInPlace(label1, label2);
succeed = true;
break;
}
// single landmark island only need one measurement
if ((dsf.isSingleton(label1)==1 && key1.type == NODE_LANDMARK_2D) ||
(dsf.isSingleton(label2)==1 && key2.type == NODE_LANDMARK_2D)) {
toErase.push_back(itFactor); nrFactors--;
dsf.makeUnionInPlace(label1, label2);
succeed = true;
break;
}
// single landmark island only need one measurement
if ((dsf.isSingleton(label1)==1 && key1.type == NODE_LANDMARK_2D) ||
(dsf.isSingleton(label2)==1 && key2.type == NODE_LANDMARK_2D)) {
toErase.push_back(itFactor); nrFactors--;
dsf.makeUnionInPlace(label1, label2);
succeed = true;
break;
}
// stack the current factor with the cached constraint
IntPair labels = (label1 < label2) ? make_pair(label1, label2) : make_pair(label2, label1);
Connections::iterator itCached = connections.find(labels);
if (itCached == connections.end()) {
connections.insert(make_pair(labels, itFactor));
continue;
} else {
GenericNode2D key21 = (*itCached->second)->key1, key22 = (*itCached->second)->key2;
// if observe the same landmark, we can not merge, abandon the current factor
if ((key1.index == key21.index && key1.type == NODE_LANDMARK_2D) ||
(key1.index == key22.index && key1.type == NODE_LANDMARK_2D) ||
(key2.index == key21.index && key2.type == NODE_LANDMARK_2D) ||
(key2.index == key22.index && key2.type == NODE_LANDMARK_2D)) {
toErase.push_back(itFactor); nrFactors--;
continue;
} else {
toErase.push_back(itFactor); nrFactors--;
toErase.push_back(itCached->second); nrFactors--;
dsf.makeUnionInPlace(label1, label2);
connections.erase(itCached);
succeed = true;
break;
}
}
}
// stack the current factor with the cached constraint
IntPair labels = (label1 < label2) ? make_pair(label1, label2) : make_pair(label2, label1);
Connections::iterator itCached = connections.find(labels);
if (itCached == connections.end()) {
connections.insert(make_pair(labels, itFactor));
continue;
} else {
GenericNode2D key21 = (*itCached->second)->key1, key22 = (*itCached->second)->key2;
// if observe the same landmark, we can not merge, abandon the current factor
if ((key1.index == key21.index && key1.type == NODE_LANDMARK_2D) ||
(key1.index == key22.index && key1.type == NODE_LANDMARK_2D) ||
(key2.index == key21.index && key2.type == NODE_LANDMARK_2D) ||
(key2.index == key22.index && key2.type == NODE_LANDMARK_2D)) {
toErase.push_back(itFactor); nrFactors--;
continue;
} else {
toErase.push_back(itFactor); nrFactors--;
toErase.push_back(itCached->second); nrFactors--;
dsf.makeUnionInPlace(label1, label2);
connections.erase(itCached);
succeed = true;
break;
}
}
}
// erase unused factors
BOOST_FOREACH(const FactorList::iterator& it, toErase)
factors.erase(it);
// erase unused factors
BOOST_FOREACH(const FactorList::iterator& it, toErase)
factors.erase(it);
if (!succeed) break;
}
if (!succeed) break;
}
list<vector<size_t> > islands;
map<size_t, vector<size_t> > arrays = dsf.arrays();
size_t key; vector<size_t> array;
BOOST_FOREACH(boost::tie(key, array), arrays)
islands.push_back(array);
return islands;
}
list<vector<size_t> > islands;
map<size_t, vector<size_t> > arrays = dsf.arrays();
size_t key; vector<size_t> array;
BOOST_FOREACH(boost::tie(key, array), arrays)
islands.push_back(array);
return islands;
}
/* ************************************************************************* */
void print(const GenericGraph2D& graph, const std::string name) {
cout << name << endl;
BOOST_FOREACH(const sharedGenericFactor2D& factor_, graph)
cout << factor_->key1.index << " " << factor_->key2.index << endl;
}
/* ************************************************************************* */
void print(const GenericGraph2D& graph, const std::string name) {
cout << name << endl;
BOOST_FOREACH(const sharedGenericFactor2D& factor_, graph)
cout << factor_->key1.index << " " << factor_->key2.index << endl;
}
/* ************************************************************************* */
void print(const GenericGraph3D& graph, const std::string name) {
cout << name << endl;
BOOST_FOREACH(const sharedGenericFactor3D& factor_, graph)
cout << factor_->key1.index << " " << factor_->key2.index << " (" <<
factor_->key1.type << ", " << factor_->key2.type <<")" << endl;
}
/* ************************************************************************* */
void print(const GenericGraph3D& graph, const std::string name) {
cout << name << endl;
BOOST_FOREACH(const sharedGenericFactor3D& factor_, graph)
cout << factor_->key1.index << " " << factor_->key2.index << " (" <<
factor_->key1.type << ", " << factor_->key2.type <<")" << endl;
}
/* ************************************************************************* */
// create disjoin set forest
DSFVector createDSF(const GenericGraph3D& graph, const vector<size_t>& keys, const WorkSpace& workspace) {
DSFVector dsf(workspace.dsf, keys);
typedef list<sharedGenericFactor3D> FactorList;
/* ************************************************************************* */
// create disjoin set forest
DSFVector createDSF(const GenericGraph3D& graph, const vector<size_t>& keys, const WorkSpace& workspace) {
DSFVector dsf(workspace.dsf, keys);
typedef list<sharedGenericFactor3D> FactorList;
FactorList factors(graph.begin(), graph.end());
size_t nrFactors = factors.size();
FactorList::iterator itEnd;
while (nrFactors) {
FactorList factors(graph.begin(), graph.end());
size_t nrFactors = factors.size();
FactorList::iterator itEnd;
while (nrFactors) {
bool succeed = false;
itEnd = factors.end();
list<FactorList::iterator> toErase;
for (FactorList::iterator itFactor=factors.begin(); itFactor!=itEnd; itFactor++) {
bool succeed = false;
itEnd = factors.end();
list<FactorList::iterator> toErase;
for (FactorList::iterator itFactor=factors.begin(); itFactor!=itEnd; itFactor++) {
// remove invalid factors
if (graph.size() == 178765) cout << "kai21" << endl;
GenericNode3D key1 = (*itFactor)->key1, key2 = (*itFactor)->key2;
if (graph.size() == 178765) cout << "kai21: " << key1.index << " " << key2.index << endl;
if (workspace.dictionary[key1.index]==-1 || workspace.dictionary[key2.index]==-1) {
toErase.push_back(itFactor); nrFactors--; continue;
}
// remove invalid factors
if (graph.size() == 178765) cout << "kai21" << endl;
GenericNode3D key1 = (*itFactor)->key1, key2 = (*itFactor)->key2;
if (graph.size() == 178765) cout << "kai21: " << key1.index << " " << key2.index << endl;
if (workspace.dictionary[key1.index]==-1 || workspace.dictionary[key2.index]==-1) {
toErase.push_back(itFactor); nrFactors--; continue;
}
if (graph.size() == 178765) cout << "kai22" << endl;
size_t label1 = dsf.findSet(key1.index);
size_t label2 = dsf.findSet(key2.index);
if (label1 == label2) { toErase.push_back(itFactor); nrFactors--; continue; }
if (graph.size() == 178765) cout << "kai22" << endl;
size_t label1 = dsf.findSet(key1.index);
size_t label2 = dsf.findSet(key2.index);
if (label1 == label2) { toErase.push_back(itFactor); nrFactors--; continue; }
if (graph.size() == 178765) cout << "kai23" << endl;
// merge two trees if the connection is strong enough, otherwise cache it
// an odometry factor always merges two islands
if ((key1.type == NODE_POSE_3D && key2.type == NODE_LANDMARK_3D) ||
(key1.type == NODE_POSE_3D && key2.type == NODE_POSE_3D)) {
toErase.push_back(itFactor); nrFactors--;
dsf.makeUnionInPlace(label1, label2);
succeed = true;
break;
}
if (graph.size() == 178765) cout << "kai23" << endl;
// merge two trees if the connection is strong enough, otherwise cache it
// an odometry factor always merges two islands
if ((key1.type == NODE_POSE_3D && key2.type == NODE_LANDMARK_3D) ||
(key1.type == NODE_POSE_3D && key2.type == NODE_POSE_3D)) {
toErase.push_back(itFactor); nrFactors--;
dsf.makeUnionInPlace(label1, label2);
succeed = true;
break;
}
if (graph.size() == 178765) cout << "kai24" << endl;
if (graph.size() == 178765) cout << "kai24" << endl;
}
}
// erase unused factors
BOOST_FOREACH(const FactorList::iterator& it, toErase)
factors.erase(it);
// erase unused factors
BOOST_FOREACH(const FactorList::iterator& it, toErase)
factors.erase(it);
if (!succeed) break;
}
return dsf;
}
if (!succeed) break;
}
return dsf;
}
/* ************************************************************************* */
// first check the type of the key (pose or landmark), and then check whether it is singular
inline bool isSingular(const set<size_t>& singularCameras, const set<size_t>& singularLandmarks, const GenericNode3D& node) {
switch(node.type) {
case NODE_POSE_3D:
return singularCameras.find(node.index) != singularCameras.end(); break;
case NODE_LANDMARK_3D:
return singularLandmarks.find(node.index) != singularLandmarks.end(); break;
default:
throw runtime_error("unrecognized key type!");
}
}
/* ************************************************************************* */
// first check the type of the key (pose or landmark), and then check whether it is singular
inline bool isSingular(const set<size_t>& singularCameras, const set<size_t>& singularLandmarks, const GenericNode3D& node) {
switch(node.type) {
case NODE_POSE_3D:
return singularCameras.find(node.index) != singularCameras.end(); break;
case NODE_LANDMARK_3D:
return singularLandmarks.find(node.index) != singularLandmarks.end(); break;
default:
throw runtime_error("unrecognized key type!");
}
}
/* ************************************************************************* */
void findSingularCamerasLandmarks(const GenericGraph3D& graph, const WorkSpace& workspace,
const vector<bool>& isCamera, const vector<bool>& isLandmark,
set<size_t>& singularCameras, set<size_t>& singularLandmarks, vector<int>& nrConstraints,
bool& foundSingularCamera, bool& foundSingularLandmark,
const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark) {
/* ************************************************************************* */
void findSingularCamerasLandmarks(const GenericGraph3D& graph, const WorkSpace& workspace,
const vector<bool>& isCamera, const vector<bool>& isLandmark,
set<size_t>& singularCameras, set<size_t>& singularLandmarks, vector<int>& nrConstraints,
bool& foundSingularCamera, bool& foundSingularLandmark,
const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark) {
// compute the constraint number per camera
std::fill(nrConstraints.begin(), nrConstraints.end(), 0);
BOOST_FOREACH(const sharedGenericFactor3D& factor_, graph) {
const int& key1 = factor_->key1.index;
const int& key2 = factor_->key2.index;
if (workspace.dictionary[key1] != -1 && workspace.dictionary[key2] != -1 &&
!isSingular(singularCameras, singularLandmarks, factor_->key1) &&
!isSingular(singularCameras, singularLandmarks, factor_->key2)) {
nrConstraints[key1]++;
nrConstraints[key2]++;
// compute the constraint number per camera
std::fill(nrConstraints.begin(), nrConstraints.end(), 0);
BOOST_FOREACH(const sharedGenericFactor3D& factor_, graph) {
const int& key1 = factor_->key1.index;
const int& key2 = factor_->key2.index;
if (workspace.dictionary[key1] != -1 && workspace.dictionary[key2] != -1 &&
!isSingular(singularCameras, singularLandmarks, factor_->key1) &&
!isSingular(singularCameras, singularLandmarks, factor_->key2)) {
nrConstraints[key1]++;
nrConstraints[key2]++;
// a single pose constraint is sufficient for stereo, so we add 2 to the counter
// for a total of 3, i.e. the same as 3 landmarks fully constraining the camera
if(factor_->key1.type == NODE_POSE_3D && factor_->key2.type == NODE_POSE_3D){
nrConstraints[key1]+=2;
nrConstraints[key2]+=2;
}
}
}
// a single pose constraint is sufficient for stereo, so we add 2 to the counter
// for a total of 3, i.e. the same as 3 landmarks fully constraining the camera
if(factor_->key1.type == NODE_POSE_3D && factor_->key2.type == NODE_POSE_3D){
nrConstraints[key1]+=2;
nrConstraints[key2]+=2;
}
}
}
// find singular cameras and landmarks
foundSingularCamera = false;
foundSingularLandmark = false;
for (size_t i=0; i<nrConstraints.size(); i++) {
if (isCamera[i] && nrConstraints[i] < minNrConstraintsPerCamera &&
singularCameras.find(i) == singularCameras.end()) {
singularCameras.insert(i);
foundSingularCamera = true;
}
if (isLandmark[i] && nrConstraints[i] < minNrConstraintsPerLandmark &&
singularLandmarks.find(i) == singularLandmarks.end()) {
singularLandmarks.insert(i);
foundSingularLandmark = true;
}
}
}
// find singular cameras and landmarks
foundSingularCamera = false;
foundSingularLandmark = false;
for (size_t i=0; i<nrConstraints.size(); i++) {
if (isCamera[i] && nrConstraints[i] < minNrConstraintsPerCamera &&
singularCameras.find(i) == singularCameras.end()) {
singularCameras.insert(i);
foundSingularCamera = true;
}
if (isLandmark[i] && nrConstraints[i] < minNrConstraintsPerLandmark &&
singularLandmarks.find(i) == singularLandmarks.end()) {
singularLandmarks.insert(i);
foundSingularLandmark = true;
}
}
}
/* ************************************************************************* */
list<vector<size_t> > findIslands(const GenericGraph3D& graph, const vector<size_t>& keys, WorkSpace& workspace,
const size_t minNrConstraintsPerCamera, const size_t minNrConstraintsPerLandmark) {
/* ************************************************************************* */
list<vector<size_t> > findIslands(const GenericGraph3D& graph, const vector<size_t>& keys, WorkSpace& workspace,
const size_t minNrConstraintsPerCamera, const size_t minNrConstraintsPerLandmark) {
// create disjoint set forest
workspace.prepareDictionary(keys);
DSFVector dsf = createDSF(graph, keys, workspace);
// create disjoint set forest
workspace.prepareDictionary(keys);
DSFVector dsf = createDSF(graph, keys, workspace);
const bool verbose = false;
bool foundSingularCamera = true;
bool foundSingularLandmark = true;
const bool verbose = false;
bool foundSingularCamera = true;
bool foundSingularLandmark = true;
list<vector<size_t> > islands;
set<size_t> singularCameras, singularLandmarks;
vector<bool> isCamera(workspace.dictionary.size(), false);
vector<bool> isLandmark(workspace.dictionary.size(), false);
list<vector<size_t> > islands;
set<size_t> singularCameras, singularLandmarks;
vector<bool> isCamera(workspace.dictionary.size(), false);
vector<bool> isLandmark(workspace.dictionary.size(), false);
// check the constraint number of every variable
// find the camera and landmark keys
BOOST_FOREACH(const sharedGenericFactor3D& factor_, graph) {
//assert(factor_->key2.type == NODE_LANDMARK_3D); // only VisualSLAM should come here, not StereoSLAM
if (workspace.dictionary[factor_->key1.index] != -1) {
if (factor_->key1.type == NODE_POSE_3D)
isCamera[factor_->key1.index] = true;
else
isLandmark[factor_->key1.index] = true;
}
// check the constraint number of every variable
// find the camera and landmark keys
BOOST_FOREACH(const sharedGenericFactor3D& factor_, graph) {
//assert(factor_->key2.type == NODE_LANDMARK_3D); // only VisualSLAM should come here, not StereoSLAM
if (workspace.dictionary[factor_->key1.index] != -1) {
if (factor_->key1.type == NODE_POSE_3D)
isCamera[factor_->key1.index] = true;
else
isLandmark[factor_->key1.index] = true;
}
if (workspace.dictionary[factor_->key2.index] != -1) {
if (factor_->key2.type == NODE_POSE_3D)
isCamera[factor_->key2.index] = true;
else
isLandmark[factor_->key2.index] = true;
if (factor_->key2.type == NODE_POSE_3D)
isCamera[factor_->key2.index] = true;
else
isLandmark[factor_->key2.index] = true;
}
}
}
vector<int> nrConstraints(workspace.dictionary.size(), 0);
// iterate until all singular variables have been removed. Removing a singular variable
// can cause another to become singular, so this will probably run several times
while (foundSingularCamera || foundSingularLandmark) {
findSingularCamerasLandmarks(graph, workspace, isCamera, isLandmark, // input
singularCameras, singularLandmarks, nrConstraints, // output
foundSingularCamera, foundSingularLandmark, // output
minNrConstraintsPerCamera, minNrConstraintsPerLandmark); // input
}
vector<int> nrConstraints(workspace.dictionary.size(), 0);
// iterate until all singular variables have been removed. Removing a singular variable
// can cause another to become singular, so this will probably run several times
while (foundSingularCamera || foundSingularLandmark) {
findSingularCamerasLandmarks(graph, workspace, isCamera, isLandmark, // input
singularCameras, singularLandmarks, nrConstraints, // output
foundSingularCamera, foundSingularLandmark, // output
minNrConstraintsPerCamera, minNrConstraintsPerLandmark); // input
}
// add singular variables directly as islands
if (!singularCameras.empty()) {
if (verbose) cout << "singular cameras:";
BOOST_FOREACH(const size_t i, singularCameras) {
islands.push_back(vector<size_t>(1, i)); // <---------------------------
if (verbose) cout << i << " ";
}
if (verbose) cout << endl;
}
if (!singularLandmarks.empty()) {
if (verbose) cout << "singular landmarks:";
BOOST_FOREACH(const size_t i, singularLandmarks) {
islands.push_back(vector<size_t>(1, i)); // <---------------------------
if (verbose) cout << i << " ";
}
if (verbose) cout << endl;
}
// add singular variables directly as islands
if (!singularCameras.empty()) {
if (verbose) cout << "singular cameras:";
BOOST_FOREACH(const size_t i, singularCameras) {
islands.push_back(vector<size_t>(1, i)); // <---------------------------
if (verbose) cout << i << " ";
}
if (verbose) cout << endl;
}
if (!singularLandmarks.empty()) {
if (verbose) cout << "singular landmarks:";
BOOST_FOREACH(const size_t i, singularLandmarks) {
islands.push_back(vector<size_t>(1, i)); // <---------------------------
if (verbose) cout << i << " ";
}
if (verbose) cout << endl;
}
// regenerating islands
map<size_t, vector<size_t> > labelIslands = dsf.arrays();
size_t label; vector<size_t> island;
BOOST_FOREACH(boost::tie(label, island), labelIslands) {
vector<size_t> filteredIsland; // remove singular cameras from array
filteredIsland.reserve(island.size());
BOOST_FOREACH(const size_t key, island) {
if ((isCamera[key] && singularCameras.find(key) == singularCameras.end()) || // not singular
(isLandmark[key] && singularLandmarks.find(key) == singularLandmarks.end()) || // not singular
(!isCamera[key] && !isLandmark[key])) { // the key is not involved in any factor, so the type is undertermined
filteredIsland.push_back(key);
}
}
islands.push_back(filteredIsland);
}
// regenerating islands
map<size_t, vector<size_t> > labelIslands = dsf.arrays();
size_t label; vector<size_t> island;
BOOST_FOREACH(boost::tie(label, island), labelIslands) {
vector<size_t> filteredIsland; // remove singular cameras from array
filteredIsland.reserve(island.size());
BOOST_FOREACH(const size_t key, island) {
if ((isCamera[key] && singularCameras.find(key) == singularCameras.end()) || // not singular
(isLandmark[key] && singularLandmarks.find(key) == singularLandmarks.end()) || // not singular
(!isCamera[key] && !isLandmark[key])) { // the key is not involved in any factor, so the type is undertermined
filteredIsland.push_back(key);
}
}
islands.push_back(filteredIsland);
}
// sanity check
size_t nrKeys = 0;
BOOST_FOREACH(const vector<size_t>& island, islands)
nrKeys += island.size();
if (nrKeys != keys.size()) {
cout << nrKeys << " vs " << keys.size() << endl;
throw runtime_error("findIslands: the number of keys is inconsistent!");
}
// sanity check
size_t nrKeys = 0;
BOOST_FOREACH(const vector<size_t>& island, islands)
nrKeys += island.size();
if (nrKeys != keys.size()) {
cout << nrKeys << " vs " << keys.size() << endl;
throw runtime_error("findIslands: the number of keys is inconsistent!");
}
if (verbose) cout << "found " << islands.size() << " islands!" << endl;
return islands;
}
if (verbose) cout << "found " << islands.size() << " islands!" << endl;
return islands;
}
/* ************************************************************************* */
// return the number of intersection between two **sorted** landmark vectors
inline int getNrCommonLandmarks(const vector<size_t>& landmarks1, const vector<size_t>& landmarks2){
size_t i1 = 0, i2 = 0;
int nrCommonLandmarks = 0;
while (i1 < landmarks1.size() && i2 < landmarks2.size()) {
if (landmarks1[i1] < landmarks2[i2])
i1 ++;
else if (landmarks1[i1] > landmarks2[i2])
i2 ++;
else {
i1++; i2++;
nrCommonLandmarks ++;
}
}
return nrCommonLandmarks;
}
/* ************************************************************************* */
// return the number of intersection between two **sorted** landmark vectors
inline int getNrCommonLandmarks(const vector<size_t>& landmarks1, const vector<size_t>& landmarks2){
size_t i1 = 0, i2 = 0;
int nrCommonLandmarks = 0;
while (i1 < landmarks1.size() && i2 < landmarks2.size()) {
if (landmarks1[i1] < landmarks2[i2])
i1 ++;
else if (landmarks1[i1] > landmarks2[i2])
i2 ++;
else {
i1++; i2++;
nrCommonLandmarks ++;
}
}
return nrCommonLandmarks;
}
/* ************************************************************************* */
void reduceGenericGraph(const GenericGraph3D& graph, const std::vector<size_t>& cameraKeys, const std::vector<size_t>& landmarkKeys,
const std::vector<int>& dictionary, GenericGraph3D& reducedGraph) {
/* ************************************************************************* */
void reduceGenericGraph(const GenericGraph3D& graph, const std::vector<size_t>& cameraKeys, const std::vector<size_t>& landmarkKeys,
const std::vector<int>& dictionary, GenericGraph3D& reducedGraph) {
typedef size_t CameraKey;
typedef pair<CameraKey, CameraKey> CameraPair;
typedef size_t LandmarkKey;
// get a mapping from each landmark to its connected cameras
vector<vector<LandmarkKey> > cameraToLandmarks(dictionary.size());
// for odometry xi-xj where i<j, we always store cameraToCamera[i] = j, otherwise equal to -1 if no odometry
vector<int> cameraToCamera(dictionary.size(), -1);
size_t key_i, key_j;
BOOST_FOREACH(const sharedGenericFactor3D& factor_, graph) {
if (factor_->key1.type == NODE_POSE_3D) {
if (factor_->key2.type == NODE_LANDMARK_3D) {// projection factor
cameraToLandmarks[factor_->key1.index].push_back(factor_->key2.index);
}
else { // odometry factor
if (factor_->key1.index < factor_->key2.index) {
key_i = factor_->key1.index;
key_j = factor_->key2.index;
} else {
key_i = factor_->key2.index;
key_j = factor_->key1.index;
}
cameraToCamera[key_i] = key_j;
}
}
}
typedef size_t CameraKey;
typedef pair<CameraKey, CameraKey> CameraPair;
typedef size_t LandmarkKey;
// get a mapping from each landmark to its connected cameras
vector<vector<LandmarkKey> > cameraToLandmarks(dictionary.size());
// for odometry xi-xj where i<j, we always store cameraToCamera[i] = j, otherwise equal to -1 if no odometry
vector<int> cameraToCamera(dictionary.size(), -1);
size_t key_i, key_j;
BOOST_FOREACH(const sharedGenericFactor3D& factor_, graph) {
if (factor_->key1.type == NODE_POSE_3D) {
if (factor_->key2.type == NODE_LANDMARK_3D) {// projection factor
cameraToLandmarks[factor_->key1.index].push_back(factor_->key2.index);
}
else { // odometry factor
if (factor_->key1.index < factor_->key2.index) {
key_i = factor_->key1.index;
key_j = factor_->key2.index;
} else {
key_i = factor_->key2.index;
key_j = factor_->key1.index;
}
cameraToCamera[key_i] = key_j;
}
}
}
// sort the landmark keys for the late getNrCommonLandmarks call
BOOST_FOREACH(vector<LandmarkKey> &landmarks, cameraToLandmarks){
if (!landmarks.empty())
std::sort(landmarks.begin(), landmarks.end());
}
// sort the landmark keys for the late getNrCommonLandmarks call
BOOST_FOREACH(vector<LandmarkKey> &landmarks, cameraToLandmarks){
if (!landmarks.empty())
std::sort(landmarks.begin(), landmarks.end());
}
// generate the reduced graph
reducedGraph.clear();
int factorIndex = 0;
int camera1, camera2, nrTotalConstraints;
bool hasOdometry;
for (size_t i1=0; i1<cameraKeys.size()-1; ++i1) {
for (size_t i2=i1+1; i2<cameraKeys.size(); ++i2) {
camera1 = cameraKeys[i1];
camera2 = cameraKeys[i2];
int nrCommonLandmarks = getNrCommonLandmarks(cameraToLandmarks[camera1], cameraToLandmarks[camera2]);
hasOdometry = cameraToCamera[camera1] == camera2;
if (nrCommonLandmarks > 0 || hasOdometry) {
nrTotalConstraints = 2 * nrCommonLandmarks + (hasOdometry ? 6 : 0);
reducedGraph.push_back(boost::make_shared<GenericFactor3D>(camera1, camera2,
factorIndex++, NODE_POSE_3D, NODE_POSE_3D, nrTotalConstraints));
}
}
}
}
// generate the reduced graph
reducedGraph.clear();
int factorIndex = 0;
int camera1, camera2, nrTotalConstraints;
bool hasOdometry;
for (size_t i1=0; i1<cameraKeys.size()-1; ++i1) {
for (size_t i2=i1+1; i2<cameraKeys.size(); ++i2) {
camera1 = cameraKeys[i1];
camera2 = cameraKeys[i2];
int nrCommonLandmarks = getNrCommonLandmarks(cameraToLandmarks[camera1], cameraToLandmarks[camera2]);
hasOdometry = cameraToCamera[camera1] == camera2;
if (nrCommonLandmarks > 0 || hasOdometry) {
nrTotalConstraints = 2 * nrCommonLandmarks + (hasOdometry ? 6 : 0);
reducedGraph.push_back(boost::make_shared<GenericFactor3D>(camera1, camera2,
factorIndex++, NODE_POSE_3D, NODE_POSE_3D, nrTotalConstraints));
}
}
}
}
/* ************************************************************************* */
void checkSingularity(const GenericGraph3D& graph, const std::vector<size_t>& frontals,
WorkSpace& workspace, const size_t minNrConstraintsPerCamera, const size_t minNrConstraintsPerLandmark) {
workspace.prepareDictionary(frontals);
vector<size_t> nrConstraints(workspace.dictionary.size(), 0);
/* ************************************************************************* */
void checkSingularity(const GenericGraph3D& graph, const std::vector<size_t>& frontals,
WorkSpace& workspace, const size_t minNrConstraintsPerCamera, const size_t minNrConstraintsPerLandmark) {
workspace.prepareDictionary(frontals);
vector<size_t> nrConstraints(workspace.dictionary.size(), 0);
// summarize the constraint number
const vector<int>& dictionary = workspace.dictionary;
vector<bool> isValidCamera(workspace.dictionary.size(), false);
vector<bool> isValidLandmark(workspace.dictionary.size(), false);
BOOST_FOREACH(const sharedGenericFactor3D& factor_, graph) {
assert(factor_->key1.type == NODE_POSE_3D);
//assert(factor_->key2.type == NODE_LANDMARK_3D);
const size_t& key1 = factor_->key1.index;
const size_t& key2 = factor_->key2.index;
if (dictionary[key1] == -1 || dictionary[key2] == -1)
continue;
// summarize the constraint number
const vector<int>& dictionary = workspace.dictionary;
vector<bool> isValidCamera(workspace.dictionary.size(), false);
vector<bool> isValidLandmark(workspace.dictionary.size(), false);
BOOST_FOREACH(const sharedGenericFactor3D& factor_, graph) {
assert(factor_->key1.type == NODE_POSE_3D);
//assert(factor_->key2.type == NODE_LANDMARK_3D);
const size_t& key1 = factor_->key1.index;
const size_t& key2 = factor_->key2.index;
if (dictionary[key1] == -1 || dictionary[key2] == -1)
continue;
isValidCamera[key1] = true;
if(factor_->key2.type == NODE_LANDMARK_3D)
isValidLandmark[key2] = true;
else
isValidCamera[key2] = true;
isValidCamera[key1] = true;
if(factor_->key2.type == NODE_LANDMARK_3D)
isValidLandmark[key2] = true;
else
isValidCamera[key2] = true;
nrConstraints[key1]++;
nrConstraints[key2]++;
nrConstraints[key1]++;
nrConstraints[key2]++;
// a single pose constraint is sufficient for stereo, so we add 2 to the counter
// for a total of 3, i.e. the same as 3 landmarks fully constraining the camera
if(factor_->key1.type == NODE_POSE_3D && factor_->key2.type == NODE_POSE_3D){
nrConstraints[key1]+=2;
nrConstraints[key2]+=2;
}
}
// a single pose constraint is sufficient for stereo, so we add 2 to the counter
// for a total of 3, i.e. the same as 3 landmarks fully constraining the camera
if(factor_->key1.type == NODE_POSE_3D && factor_->key2.type == NODE_POSE_3D){
nrConstraints[key1]+=2;
nrConstraints[key2]+=2;
}
}
// find the minimum constraint for cameras and landmarks
size_t minFoundConstraintsPerCamera = 10000;
size_t minFoundConstraintsPerLandmark = 10000;
// find the minimum constraint for cameras and landmarks
size_t minFoundConstraintsPerCamera = 10000;
size_t minFoundConstraintsPerLandmark = 10000;
for (size_t i=0; i<isValidCamera.size(); i++) {
if (isValidCamera[i]) {
minFoundConstraintsPerCamera = std::min(nrConstraints[i], minFoundConstraintsPerCamera);
if (nrConstraints[i] < minNrConstraintsPerCamera)
cout << "!!!!!!!!!!!!!!!!!!! camera with " << nrConstraints[i] << " constraint: " << i << endl;
}
for (size_t i=0; i<isValidCamera.size(); i++) {
if (isValidCamera[i]) {
minFoundConstraintsPerCamera = std::min(nrConstraints[i], minFoundConstraintsPerCamera);
if (nrConstraints[i] < minNrConstraintsPerCamera)
cout << "!!!!!!!!!!!!!!!!!!! camera with " << nrConstraints[i] << " constraint: " << i << endl;
}
}
for (size_t j=0; j<isValidLandmark.size(); j++) {
if (isValidLandmark[j]) {
minFoundConstraintsPerLandmark = std::min(nrConstraints[j], minFoundConstraintsPerLandmark);
if (nrConstraints[j] < minNrConstraintsPerLandmark)
cout << "!!!!!!!!!!!!!!!!!!! landmark with " << nrConstraints[j] << " constraint: " << j << endl;
}
}
}
for (size_t j=0; j<isValidLandmark.size(); j++) {
if (isValidLandmark[j]) {
minFoundConstraintsPerLandmark = std::min(nrConstraints[j], minFoundConstraintsPerLandmark);
if (nrConstraints[j] < minNrConstraintsPerLandmark)
cout << "!!!!!!!!!!!!!!!!!!! landmark with " << nrConstraints[j] << " constraint: " << j << endl;
}
}
// debug info
BOOST_FOREACH(const size_t key, frontals) {
if (isValidCamera[key] && nrConstraints[key] < minNrConstraintsPerCamera)
cout << "singular camera:" << key << " with " << nrConstraints[key] << " constraints" << endl;
}
// debug info
BOOST_FOREACH(const size_t key, frontals) {
if (isValidCamera[key] && nrConstraints[key] < minNrConstraintsPerCamera)
cout << "singular camera:" << key << " with " << nrConstraints[key] << " constraints" << endl;
}
if (minFoundConstraintsPerCamera < minNrConstraintsPerCamera)
throw runtime_error("checkSingularity:minConstraintsPerCamera < " + boost::lexical_cast<string>(minFoundConstraintsPerCamera));
if (minFoundConstraintsPerLandmark < minNrConstraintsPerLandmark)
throw runtime_error("checkSingularity:minConstraintsPerLandmark < " + boost::lexical_cast<string>(minFoundConstraintsPerLandmark));
}
if (minFoundConstraintsPerCamera < minNrConstraintsPerCamera)
throw runtime_error("checkSingularity:minConstraintsPerCamera < " + boost::lexical_cast<string>(minFoundConstraintsPerCamera));
if (minFoundConstraintsPerLandmark < minNrConstraintsPerLandmark)
throw runtime_error("checkSingularity:minConstraintsPerLandmark < " + boost::lexical_cast<string>(minFoundConstraintsPerLandmark));
}
}} // namespace

216
gtsam_unstable/partition/GenericGraph.h
View File

@ -17,133 +17,133 @@
namespace gtsam { namespace partition {
/***************************************************
* 2D generic factors and their factor graph
***************************************************/
enum GenericNode2DType { NODE_POSE_2D, NODE_LANDMARK_2D };
/***************************************************
* 2D generic factors and their factor graph
***************************************************/
enum GenericNode2DType { NODE_POSE_2D, NODE_LANDMARK_2D };
/** the index of the node and the type of the node */
struct GenericNode2D {
std::size_t index;
GenericNode2DType type;
GenericNode2D (const std::size_t& index_in, const GenericNode2DType& type_in) : index(index_in), type(type_in) {}
};
/** the index of the node and the type of the node */
struct GenericNode2D {
std::size_t index;
GenericNode2DType type;
GenericNode2D (const std::size_t& index_in, const GenericNode2DType& type_in) : index(index_in), type(type_in) {}
};
/** a factor always involves two nodes/variables for now */
struct GenericFactor2D {
GenericNode2D key1;
GenericNode2D key2;
int index; // the factor index in the original nonlinear factor graph
int weight; // the weight of the edge
GenericFactor2D(const size_t index1, const GenericNode2DType type1, const size_t index2, const GenericNode2DType type2, const int index_ = -1, const int weight_ = 1)
: key1(index1, type1), key2(index2, type2), index(index_), weight(weight_) {}
GenericFactor2D(const size_t index1, const char type1, const size_t index2, const char type2, const int index_ = -1, const int weight_ = 1)
: key1(index1, type1 == 'x' ? NODE_POSE_2D : NODE_LANDMARK_2D),
key2(index2, type2 == 'x' ? NODE_POSE_2D : NODE_LANDMARK_2D), index(index_), weight(weight_) {}
};
/** a factor always involves two nodes/variables for now */
struct GenericFactor2D {
GenericNode2D key1;
GenericNode2D key2;
int index; // the factor index in the original nonlinear factor graph
int weight; // the weight of the edge
GenericFactor2D(const size_t index1, const GenericNode2DType type1, const size_t index2, const GenericNode2DType type2, const int index_ = -1, const int weight_ = 1)
: key1(index1, type1), key2(index2, type2), index(index_), weight(weight_) {}
GenericFactor2D(const size_t index1, const char type1, const size_t index2, const char type2, const int index_ = -1, const int weight_ = 1)
: key1(index1, type1 == 'x' ? NODE_POSE_2D : NODE_LANDMARK_2D),
key2(index2, type2 == 'x' ? NODE_POSE_2D : NODE_LANDMARK_2D), index(index_), weight(weight_) {}
};
/** graph is a collection of factors */
typedef boost::shared_ptr<GenericFactor2D> sharedGenericFactor2D;
typedef std::vector<sharedGenericFactor2D> GenericGraph2D;
/** graph is a collection of factors */
typedef boost::shared_ptr<GenericFactor2D> sharedGenericFactor2D;
typedef std::vector<sharedGenericFactor2D> GenericGraph2D;
/** merge nodes in DSF using constraints captured by the given graph */
std::list<std::vector<size_t> > findIslands(const GenericGraph2D& graph, const std::vector<size_t>& keys, WorkSpace& workspace,
const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark);
/** merge nodes in DSF using constraints captured by the given graph */
std::list<std::vector<size_t> > findIslands(const GenericGraph2D& graph, const std::vector<size_t>& keys, WorkSpace& workspace,
const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark);
/** eliminate the sensors from generic graph */
inline void reduceGenericGraph(const GenericGraph2D& graph, const std::vector<size_t>& cameraKeys, const std::vector<size_t>& landmarkKeys,
const std::vector<int>& dictionary, GenericGraph2D& reducedGraph) {
throw std::runtime_error("reduceGenericGraph 2d not implemented");
}
/** eliminate the sensors from generic graph */
inline void reduceGenericGraph(const GenericGraph2D& graph, const std::vector<size_t>& cameraKeys, const std::vector<size_t>& landmarkKeys,
const std::vector<int>& dictionary, GenericGraph2D& reducedGraph) {
throw std::runtime_error("reduceGenericGraph 2d not implemented");
}
/** check whether the 2D graph is singular (under constrained) , Dummy function for 2D */
inline void checkSingularity(const GenericGraph2D& graph, const std::vector<size_t>& frontals,
WorkSpace& workspace, const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark) { return; }
/** check whether the 2D graph is singular (under constrained) , Dummy function for 2D */
inline void checkSingularity(const GenericGraph2D& graph, const std::vector<size_t>& frontals,
WorkSpace& workspace, const int minNrConstraintsPerCamera, const int minNrConstraintsPerLandmark) { return; }
/** print the graph **/
void print(const GenericGraph2D& graph, const std::string name = "GenericGraph2D");
/** print the graph **/
void print(const GenericGraph2D& graph, const std::string name = "GenericGraph2D");
/***************************************************
* 3D generic factors and their factor graph
***************************************************/
enum GenericNode3DType { NODE_POSE_3D, NODE_LANDMARK_3D };
/***************************************************
* 3D generic factors and their factor graph
***************************************************/
enum GenericNode3DType { NODE_POSE_3D, NODE_LANDMARK_3D };
// const int minNrConstraintsPerCamera = 7;
// const int minNrConstraintsPerLandmark = 2;
// const int minNrConstraintsPerCamera = 7;
// const int minNrConstraintsPerLandmark = 2;
/** the index of the node and the type of the node */
struct GenericNode3D {
std::size_t index;
GenericNode3DType type;
GenericNode3D (const std::size_t& index_in, const GenericNode3DType& type_in) : index(index_in), type(type_in) {}
};
/** the index of the node and the type of the node */
struct GenericNode3D {
std::size_t index;
GenericNode3DType type;
GenericNode3D (const std::size_t& index_in, const GenericNode3DType& type_in) : index(index_in), type(type_in) {}
};
/** a factor always involves two nodes/variables for now */
struct GenericFactor3D {
GenericNode3D key1;
GenericNode3D key2;
int index; // the index in the entire graph, 0-based
int weight; // the weight of the edge
GenericFactor3D() :key1(-1, NODE_POSE_3D), key2(-1, NODE_LANDMARK_3D), index(-1), weight(1) {}
GenericFactor3D(const size_t index1, const size_t index2, const int index_ = -1,
const GenericNode3DType type1 = NODE_POSE_3D, const GenericNode3DType type2 = NODE_LANDMARK_3D, const int weight_ = 1)
: key1(index1, type1), key2(index2, type2), index(index_), weight(weight_) {}
};
/** a factor always involves two nodes/variables for now */
struct GenericFactor3D {
GenericNode3D key1;
GenericNode3D key2;
int index; // the index in the entire graph, 0-based
int weight; // the weight of the edge
GenericFactor3D() :key1(-1, NODE_POSE_3D), key2(-1, NODE_LANDMARK_3D), index(-1), weight(1) {}
GenericFactor3D(const size_t index1, const size_t index2, const int index_ = -1,
const GenericNode3DType type1 = NODE_POSE_3D, const GenericNode3DType type2 = NODE_LANDMARK_3D, const int weight_ = 1)
: key1(index1, type1), key2(index2, type2), index(index_), weight(weight_) {}
};
/** graph is a collection of factors */
typedef boost::shared_ptr<GenericFactor3D> sharedGenericFactor3D;
typedef std::vector<sharedGenericFactor3D> GenericGraph3D;
/** graph is a collection of factors */
typedef boost::shared_ptr<GenericFactor3D> sharedGenericFactor3D;
typedef std::vector<sharedGenericFactor3D> GenericGraph3D;
/** merge nodes in DSF using constraints captured by the given graph */
std::list<std::vector<size_t> > findIslands(const GenericGraph3D& graph, const std::vector<size_t>& keys, WorkSpace& workspace,
const size_t minNrConstraintsPerCamera, const size_t minNrConstraintsPerLandmark);
/** merge nodes in DSF using constraints captured by the given graph */
std::list<std::vector<size_t> > findIslands(const GenericGraph3D& graph, const std::vector<size_t>& keys, WorkSpace& workspace,
const size_t minNrConstraintsPerCamera, const size_t minNrConstraintsPerLandmark);
/** eliminate the sensors from generic graph */
void reduceGenericGraph(const GenericGraph3D& graph, const std::vector<size_t>& cameraKeys, const std::vector<size_t>& landmarkKeys,
const std::vector<int>& dictionary, GenericGraph3D& reducedGraph);
/** eliminate the sensors from generic graph */
void reduceGenericGraph(const GenericGraph3D& graph, const std::vector<size_t>& cameraKeys, const std::vector<size_t>& landmarkKeys,
const std::vector<int>& dictionary, GenericGraph3D& reducedGraph);
/** check whether the 3D graph is singular (under constrained) */
void checkSingularity(const GenericGraph3D& graph, const std::vector<size_t>& frontals,
WorkSpace& workspace, const size_t minNrConstraintsPerCamera, const size_t minNrConstraintsPerLandmark);
/** check whether the 3D graph is singular (under constrained) */
void checkSingularity(const GenericGraph3D& graph, const std::vector<size_t>& frontals,
WorkSpace& workspace, const size_t minNrConstraintsPerCamera, const size_t minNrConstraintsPerLandmark);
/** print the graph **/
void print(const GenericGraph3D& graph, const std::string name = "GenericGraph3D");
/** print the graph **/
void print(const GenericGraph3D& graph, const std::string name = "GenericGraph3D");
/***************************************************
* unary generic factors and their factor graph
***************************************************/
/** a factor involves a single variable */
struct GenericUnaryFactor {
GenericNode2D key;
int index; // the factor index in the original nonlinear factor graph
GenericUnaryFactor(const size_t key_, const GenericNode2DType type_, const int index_ = -1)
: key(key_, type_), index(index_) {}
GenericUnaryFactor(const size_t key_, const char type_, const int index_ = -1)
: key(key_, type_ == 'x' ? NODE_POSE_2D : NODE_LANDMARK_2D), index(index_) {}
};
/***************************************************
* unary generic factors and their factor graph
***************************************************/
/** a factor involves a single variable */
struct GenericUnaryFactor {
GenericNode2D key;
int index; // the factor index in the original nonlinear factor graph
GenericUnaryFactor(const size_t key_, const GenericNode2DType type_, const int index_ = -1)
: key(key_, type_), index(index_) {}
GenericUnaryFactor(const size_t key_, const char type_, const int index_ = -1)
: key(key_, type_ == 'x' ? NODE_POSE_2D : NODE_LANDMARK_2D), index(index_) {}
};
/** graph is a collection of factors */
typedef boost::shared_ptr<GenericUnaryFactor> sharedGenericUnaryFactor;
typedef std::vector<sharedGenericUnaryFactor> GenericUnaryGraph;
/** graph is a collection of factors */
typedef boost::shared_ptr<GenericUnaryFactor> sharedGenericUnaryFactor;
typedef std::vector<sharedGenericUnaryFactor> GenericUnaryGraph;
/***************************************************
* utility functions
***************************************************/
inline bool hasCommonCamera(const std::set<size_t>& cameras1, const std::set<size_t>& cameras2) {
if (cameras1.empty() || cameras2.empty())
throw std::invalid_argument("hasCommonCamera: the input camera set is empty!");
std::set<size_t>::const_iterator it1 = cameras1.begin();
std::set<size_t>::const_iterator it2 = cameras2.begin();
while (it1 != cameras1.end() && it2 != cameras2.end()) {
if (*it1 == *it2)
return true;
else if (*it1 < *it2)
it1++;
else
it2++;
}
return false;
}
/***************************************************
* utility functions
***************************************************/
inline bool hasCommonCamera(const std::set<size_t>& cameras1, const std::set<size_t>& cameras2) {
if (cameras1.empty() || cameras2.empty())
throw std::invalid_argument("hasCommonCamera: the input camera set is empty!");
std::set<size_t>::const_iterator it1 = cameras1.begin();
std::set<size_t>::const_iterator it2 = cameras2.begin();
while (it1 != cameras1.end() && it2 != cameras2.end()) {
if (*it1 == *it2)
return true;
else if (*it1 < *it2)
it1++;
else
it2++;
}
return false;
}
}} // namespace

404
gtsam_unstable/partition/NestedDissection-inl.h
View File

@ -16,236 +16,236 @@
namespace gtsam { namespace partition {
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
NestedDissection<NLG, SubNLG, GenericGraph>::NestedDissection(
const NLG& fg, const Ordering& ordering, const int numNodeStopPartition, const int minNodesPerMap, const bool verbose) :
fg_(fg), ordering_(ordering){
GenericUnaryGraph unaryFactors;
GenericGraph gfg;
boost::tie(unaryFactors, gfg) = fg.createGenericGraph(ordering);
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
NestedDissection<NLG, SubNLG, GenericGraph>::NestedDissection(
const NLG& fg, const Ordering& ordering, const int numNodeStopPartition, const int minNodesPerMap, const bool verbose) :
fg_(fg), ordering_(ordering){
GenericUnaryGraph unaryFactors;
GenericGraph gfg;
boost::tie(unaryFactors, gfg) = fg.createGenericGraph(ordering);
// build reverse mapping from integer to symbol
int numNodes = ordering.size();
int2symbol_.resize(numNodes);
Ordering::const_iterator it = ordering.begin(), itLast = ordering.end();
while(it != itLast)
int2symbol_[it->second] = (it++)->first;
// build reverse mapping from integer to symbol
int numNodes = ordering.size();
int2symbol_.resize(numNodes);
Ordering::const_iterator it = ordering.begin(), itLast = ordering.end();
while(it != itLast)
int2symbol_[it->second] = (it++)->first;
vector<size_t> keys;
keys.reserve(numNodes);
for(int i=0; i<ordering.size(); ++i)
keys.push_back(i);
vector<size_t> keys;
keys.reserve(numNodes);
for(int i=0; i<ordering.size(); ++i)
keys.push_back(i);
WorkSpace workspace(numNodes);
root_ = recursivePartition(gfg, unaryFactors, keys, vector<size_t>(), numNodeStopPartition, minNodesPerMap, boost::shared_ptr<SubNLG>(), workspace, verbose);
}
WorkSpace workspace(numNodes);
root_ = recursivePartition(gfg, unaryFactors, keys, vector<size_t>(), numNodeStopPartition, minNodesPerMap, boost::shared_ptr<SubNLG>(), workspace, verbose);
}
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
NestedDissection<NLG, SubNLG, GenericGraph>::NestedDissection(
const NLG& fg, const Ordering& ordering, const boost::shared_ptr<Cuts>& cuts, const bool verbose) : fg_(fg), ordering_(ordering){
GenericUnaryGraph unaryFactors;
GenericGraph gfg;
boost::tie(unaryFactors, gfg) = fg.createGenericGraph(ordering);
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
NestedDissection<NLG, SubNLG, GenericGraph>::NestedDissection(
const NLG& fg, const Ordering& ordering, const boost::shared_ptr<Cuts>& cuts, const bool verbose) : fg_(fg), ordering_(ordering){
GenericUnaryGraph unaryFactors;
GenericGraph gfg;
boost::tie(unaryFactors, gfg) = fg.createGenericGraph(ordering);
// build reverse mapping from integer to symbol
int numNodes = ordering.size();
int2symbol_.resize(numNodes);
Ordering::const_iterator it = ordering.begin(), itLast = ordering.end();
while(it != itLast)
int2symbol_[it->second] = (it++)->first;
// build reverse mapping from integer to symbol
int numNodes = ordering.size();
int2symbol_.resize(numNodes);
Ordering::const_iterator it = ordering.begin(), itLast = ordering.end();
while(it != itLast)
int2symbol_[it->second] = (it++)->first;
vector<size_t> keys;
keys.reserve(numNodes);
for(int i=0; i<ordering.size(); ++i)
keys.push_back(i);
vector<size_t> keys;
keys.reserve(numNodes);
for(int i=0; i<ordering.size(); ++i)
keys.push_back(i);
WorkSpace workspace(numNodes);
root_ = recursivePartition(gfg, unaryFactors, keys, vector<size_t>(), cuts, boost::shared_ptr<SubNLG>(), workspace, verbose);
}
WorkSpace workspace(numNodes);
root_ = recursivePartition(gfg, unaryFactors, keys, vector<size_t>(), cuts, boost::shared_ptr<SubNLG>(), workspace, verbose);
}
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
boost::shared_ptr<SubNLG> NestedDissection<NLG, SubNLG, GenericGraph>::makeSubNLG(
const NLG& fg, const vector<size_t>& frontals, const vector<size_t>& sep, const boost::shared_ptr<SubNLG>& parent) const {
OrderedSymbols frontalKeys;
BOOST_FOREACH(const size_t index, frontals)
frontalKeys.push_back(int2symbol_[index]);
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
boost::shared_ptr<SubNLG> NestedDissection<NLG, SubNLG, GenericGraph>::makeSubNLG(
const NLG& fg, const vector<size_t>& frontals, const vector<size_t>& sep, const boost::shared_ptr<SubNLG>& parent) const {
OrderedSymbols frontalKeys;
BOOST_FOREACH(const size_t index, frontals)
frontalKeys.push_back(int2symbol_[index]);
UnorderedSymbols sepKeys;
BOOST_FOREACH(const size_t index, sep)
sepKeys.insert(int2symbol_[index]);
UnorderedSymbols sepKeys;
BOOST_FOREACH(const size_t index, sep)
sepKeys.insert(int2symbol_[index]);
return boost::make_shared<SubNLG>(fg, frontalKeys, sepKeys, parent);
}
return boost::make_shared<SubNLG>(fg, frontalKeys, sepKeys, parent);
}
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
void NestedDissection<NLG, SubNLG, GenericGraph>::processFactor(
const typename GenericGraph::value_type& factor, const std::vector<int>& partitionTable, // input
vector<GenericGraph>& frontalFactors, NLG& sepFactors, vector<set<size_t> >& childSeps, // output factor graphs
typename SubNLG::Weeklinks& weeklinks) const { // the links between child cliques
list<size_t> sep_; // the separator variables involved in the current factor
int partition1 = partitionTable[factor->key1.index];
int partition2 = partitionTable[factor->key2.index];
if (partition1 <= 0 && partition2 <= 0) { // is a factor in the current clique
sepFactors.push_back(fg_[factor->index]);
}
else if (partition1 > 0 && partition2 > 0 && partition1 != partition2) { // is a weeklink (factor between two child cliques)
weeklinks.push_back(fg_[factor->index]);
}
else if (partition1 > 0 && partition2 > 0 && partition1 == partition2) { // is a local factor in one of the child cliques
frontalFactors[partition1 - 1].push_back(factor);
}
else { // is a joint factor in the child clique (involving varaibles in the current clique)
if (partition1 > 0 && partition2 <= 0) {
frontalFactors[partition1 - 1].push_back(factor);
childSeps[partition1 - 1].insert(factor->key2.index);
} else if (partition1 <= 0 && partition2 > 0) {
frontalFactors[partition2 - 1].push_back(factor);
childSeps[partition2 - 1].insert(factor->key1.index);
} else
throw runtime_error("processFactor: unexpected entries in the partition table!");
}
}
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
void NestedDissection<NLG, SubNLG, GenericGraph>::processFactor(
const typename GenericGraph::value_type& factor, const std::vector<int>& partitionTable, // input
vector<GenericGraph>& frontalFactors, NLG& sepFactors, vector<set<size_t> >& childSeps, // output factor graphs
typename SubNLG::Weeklinks& weeklinks) const { // the links between child cliques
list<size_t> sep_; // the separator variables involved in the current factor
int partition1 = partitionTable[factor->key1.index];
int partition2 = partitionTable[factor->key2.index];
if (partition1 <= 0 && partition2 <= 0) { // is a factor in the current clique
sepFactors.push_back(fg_[factor->index]);
}
else if (partition1 > 0 && partition2 > 0 && partition1 != partition2) { // is a weeklink (factor between two child cliques)
weeklinks.push_back(fg_[factor->index]);
}
else if (partition1 > 0 && partition2 > 0 && partition1 == partition2) { // is a local factor in one of the child cliques
frontalFactors[partition1 - 1].push_back(factor);
}
else { // is a joint factor in the child clique (involving varaibles in the current clique)
if (partition1 > 0 && partition2 <= 0) {
frontalFactors[partition1 - 1].push_back(factor);
childSeps[partition1 - 1].insert(factor->key2.index);
} else if (partition1 <= 0 && partition2 > 0) {
frontalFactors[partition2 - 1].push_back(factor);
childSeps[partition2 - 1].insert(factor->key1.index);
} else
throw runtime_error("processFactor: unexpected entries in the partition table!");
}
}
/* ************************************************************************* */
/**
* given a factor graph and its partition {nodeMap}, split the factors between the child cliques ({frontalFactors})
* and the current clique ({sepFactors}). Also split the variables between the child cliques ({childFrontals})
* and the current clique ({localFrontals}). Those separator variables involved in {frontalFactors} are put into
* the correspoding ordering in {childSeps}.
*/
// TODO: frontalFactors and localFrontals should be generated in findSeparator
template <class NLG, class SubNLG, class GenericGraph>
void NestedDissection<NLG, SubNLG, GenericGraph>::partitionFactorsAndVariables(
const GenericGraph& fg, const GenericUnaryGraph& unaryFactors, const std::vector<size_t>& keys, //input
const std::vector<int>& partitionTable, const int numSubmaps, // input
vector<GenericGraph>& frontalFactors, vector<GenericUnaryGraph>& frontalUnaryFactors, NLG& sepFactors, // output factor graphs
vector<vector<size_t> >& childFrontals, vector<vector<size_t> >& childSeps, vector<size_t>& localFrontals, // output sub-orderings
typename SubNLG::Weeklinks& weeklinks) const { // the links between child cliques
/* ************************************************************************* */
/**
* given a factor graph and its partition {nodeMap}, split the factors between the child cliques ({frontalFactors})
* and the current clique ({sepFactors}). Also split the variables between the child cliques ({childFrontals})
* and the current clique ({localFrontals}). Those separator variables involved in {frontalFactors} are put into
* the correspoding ordering in {childSeps}.
*/
// TODO: frontalFactors and localFrontals should be generated in findSeparator
template <class NLG, class SubNLG, class GenericGraph>
void NestedDissection<NLG, SubNLG, GenericGraph>::partitionFactorsAndVariables(
const GenericGraph& fg, const GenericUnaryGraph& unaryFactors, const std::vector<size_t>& keys, //input
const std::vector<int>& partitionTable, const int numSubmaps, // input
vector<GenericGraph>& frontalFactors, vector<GenericUnaryGraph>& frontalUnaryFactors, NLG& sepFactors, // output factor graphs
vector<vector<size_t> >& childFrontals, vector<vector<size_t> >& childSeps, vector<size_t>& localFrontals, // output sub-orderings
typename SubNLG::Weeklinks& weeklinks) const { // the links between child cliques
// make three lists of variables A, B, and C
int partition;
childFrontals.resize(numSubmaps);
BOOST_FOREACH(const size_t key, keys){
partition = partitionTable[key];
switch (partition) {
case -1: break; // the separator of the separator variables
case 0: localFrontals.push_back(key); break; // the separator variables
default: childFrontals[partition-1].push_back(key); // the frontal variables
}
}
// make three lists of variables A, B, and C
int partition;
childFrontals.resize(numSubmaps);
BOOST_FOREACH(const size_t key, keys){
partition = partitionTable[key];
switch (partition) {
case -1: break; // the separator of the separator variables
case 0: localFrontals.push_back(key); break; // the separator variables
default: childFrontals[partition-1].push_back(key); // the frontal variables
}
}
// group the factors to {frontalFactors} and {sepFactors},and find the joint variables
vector<set<size_t> > childSeps_;
childSeps_.resize(numSubmaps);
childSeps.reserve(numSubmaps);
frontalFactors.resize(numSubmaps);
frontalUnaryFactors.resize(numSubmaps);
BOOST_FOREACH(typename GenericGraph::value_type factor, fg)
processFactor(factor, partitionTable, frontalFactors, sepFactors, childSeps_, weeklinks);
BOOST_FOREACH(const set<size_t>& childSep, childSeps_)
childSeps.push_back(vector<size_t>(childSep.begin(), childSep.end()));
// group the factors to {frontalFactors} and {sepFactors},and find the joint variables
vector<set<size_t> > childSeps_;
childSeps_.resize(numSubmaps);
childSeps.reserve(numSubmaps);
frontalFactors.resize(numSubmaps);
frontalUnaryFactors.resize(numSubmaps);
BOOST_FOREACH(typename GenericGraph::value_type factor, fg)
processFactor(factor, partitionTable, frontalFactors, sepFactors, childSeps_, weeklinks);
BOOST_FOREACH(const set<size_t>& childSep, childSeps_)
childSeps.push_back(vector<size_t>(childSep.begin(), childSep.end()));
// add unary factor to the current cluster or pass it to one of the child clusters
BOOST_FOREACH(const sharedGenericUnaryFactor& unaryFactor_, unaryFactors) {
partition = partitionTable[unaryFactor_->key.index];
if (!partition) sepFactors.push_back(fg_[unaryFactor_->index]);
else frontalUnaryFactors[partition-1].push_back(unaryFactor_);
}
}
// add unary factor to the current cluster or pass it to one of the child clusters
BOOST_FOREACH(const sharedGenericUnaryFactor& unaryFactor_, unaryFactors) {
partition = partitionTable[unaryFactor_->key.index];
if (!partition) sepFactors.push_back(fg_[unaryFactor_->index]);
else frontalUnaryFactors[partition-1].push_back(unaryFactor_);
}
}
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
NLG NestedDissection<NLG, SubNLG, GenericGraph>::collectOriginalFactors(
const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors) const {
NLG sepFactors;
typename GenericGraph::const_iterator it = gfg.begin(), itLast = gfg.end();
while(it!=itLast) sepFactors.push_back(fg_[(*it++)->index]);
BOOST_FOREACH(const sharedGenericUnaryFactor& unaryFactor_, unaryFactors)
sepFactors.push_back(fg_[unaryFactor_->index]);
return sepFactors;
}
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
NLG NestedDissection<NLG, SubNLG, GenericGraph>::collectOriginalFactors(
const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors) const {
NLG sepFactors;
typename GenericGraph::const_iterator it = gfg.begin(), itLast = gfg.end();
while(it!=itLast) sepFactors.push_back(fg_[(*it++)->index]);
BOOST_FOREACH(const sharedGenericUnaryFactor& unaryFactor_, unaryFactors)
sepFactors.push_back(fg_[unaryFactor_->index]);
return sepFactors;
}
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
boost::shared_ptr<SubNLG> NestedDissection<NLG, SubNLG, GenericGraph>::recursivePartition(
const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors, const vector<size_t>& frontals, const vector<size_t>& sep,
const int numNodeStopPartition, const int minNodesPerMap, const boost::shared_ptr<SubNLG>& parent, WorkSpace& workspace, const bool verbose) const {
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
boost::shared_ptr<SubNLG> NestedDissection<NLG, SubNLG, GenericGraph>::recursivePartition(
const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors, const vector<size_t>& frontals, const vector<size_t>& sep,
const int numNodeStopPartition, const int minNodesPerMap, const boost::shared_ptr<SubNLG>& parent, WorkSpace& workspace, const bool verbose) const {
// if no split needed
NLG sepFactors; // factors that should remain in the current cluster
if (frontals.size() <= numNodeStopPartition || gfg.size() <= numNodeStopPartition) {
sepFactors = collectOriginalFactors(gfg, unaryFactors);
return makeSubNLG(sepFactors, frontals, sep, parent);
}
// if no split needed
NLG sepFactors; // factors that should remain in the current cluster
if (frontals.size() <= numNodeStopPartition || gfg.size() <= numNodeStopPartition) {
sepFactors = collectOriginalFactors(gfg, unaryFactors);
return makeSubNLG(sepFactors, frontals, sep, parent);
}
// find the nested dissection separator
int numSubmaps = findSeparator(gfg, frontals, minNodesPerMap, workspace, verbose, int2symbol_, NLG::reduceGraph(),
NLG::minNrConstraintsPerCamera(),NLG::minNrConstraintsPerLandmark());
partition::PartitionTable& partitionTable = workspace.partitionTable;
if (numSubmaps == 0) throw runtime_error("recursivePartition: get zero submap after ND!");
// find the nested dissection separator
int numSubmaps = findSeparator(gfg, frontals, minNodesPerMap, workspace, verbose, int2symbol_, NLG::reduceGraph(),
NLG::minNrConstraintsPerCamera(),NLG::minNrConstraintsPerLandmark());
partition::PartitionTable& partitionTable = workspace.partitionTable;
if (numSubmaps == 0) throw runtime_error("recursivePartition: get zero submap after ND!");
// split the factors between child cliques and the current clique
vector<GenericGraph> frontalFactors; vector<GenericUnaryGraph> frontalUnaryFactors; typename SubNLG::Weeklinks weeklinks;
vector<size_t> localFrontals; vector<vector<size_t> > childFrontals, childSeps;
partitionFactorsAndVariables(gfg, unaryFactors, frontals, partitionTable, numSubmaps,
frontalFactors, frontalUnaryFactors, sepFactors, childFrontals, childSeps, localFrontals, weeklinks);
// split the factors between child cliques and the current clique
vector<GenericGraph> frontalFactors; vector<GenericUnaryGraph> frontalUnaryFactors; typename SubNLG::Weeklinks weeklinks;
vector<size_t> localFrontals; vector<vector<size_t> > childFrontals, childSeps;
partitionFactorsAndVariables(gfg, unaryFactors, frontals, partitionTable, numSubmaps,
frontalFactors, frontalUnaryFactors, sepFactors, childFrontals, childSeps, localFrontals, weeklinks);
// make a new cluster
boost::shared_ptr<SubNLG> current = makeSubNLG(sepFactors, localFrontals, sep, parent);
current->setWeeklinks(weeklinks);
// make a new cluster
boost::shared_ptr<SubNLG> current = makeSubNLG(sepFactors, localFrontals, sep, parent);
current->setWeeklinks(weeklinks);
// check whether all the submaps are fully constrained
for (int i=0; i<numSubmaps; i++) {
checkSingularity(frontalFactors[i], childFrontals[i], workspace, NLG::minNrConstraintsPerCamera(),NLG::minNrConstraintsPerLandmark());
}
// check whether all the submaps are fully constrained
for (int i=0; i<numSubmaps; i++) {
checkSingularity(frontalFactors[i], childFrontals[i], workspace, NLG::minNrConstraintsPerCamera(),NLG::minNrConstraintsPerLandmark());
}
// create child clusters
for (int i=0; i<numSubmaps; i++) {
boost::shared_ptr<SubNLG> child = recursivePartition(frontalFactors[i], frontalUnaryFactors[i], childFrontals[i], childSeps[i],
numNodeStopPartition, minNodesPerMap, current, workspace, verbose);
current->addChild(child);
}
// create child clusters
for (int i=0; i<numSubmaps; i++) {
boost::shared_ptr<SubNLG> child = recursivePartition(frontalFactors[i], frontalUnaryFactors[i], childFrontals[i], childSeps[i],
numNodeStopPartition, minNodesPerMap, current, workspace, verbose);
current->addChild(child);
}
return current;
}
return current;
}
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
boost::shared_ptr<SubNLG> NestedDissection<NLG, SubNLG, GenericGraph>::recursivePartition(
const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors, const vector<size_t>& frontals, const vector<size_t>& sep,
const boost::shared_ptr<Cuts>& cuts, const boost::shared_ptr<SubNLG>& parent, WorkSpace& workspace, const bool verbose) const {
/* ************************************************************************* */
template <class NLG, class SubNLG, class GenericGraph>
boost::shared_ptr<SubNLG> NestedDissection<NLG, SubNLG, GenericGraph>::recursivePartition(
const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors, const vector<size_t>& frontals, const vector<size_t>& sep,
const boost::shared_ptr<Cuts>& cuts, const boost::shared_ptr<SubNLG>& parent, WorkSpace& workspace, const bool verbose) const {
// if there is no need to cut any more
NLG sepFactors; // factors that should remain in the current cluster
if (!cuts.get()) {
sepFactors = collectOriginalFactors(gfg, unaryFactors);
return makeSubNLG(sepFactors, frontals, sep, parent);
}
// if there is no need to cut any more
NLG sepFactors; // factors that should remain in the current cluster
if (!cuts.get()) {
sepFactors = collectOriginalFactors(gfg, unaryFactors);
return makeSubNLG(sepFactors, frontals, sep, parent);
}
// retrieve the current partitioning info
int numSubmaps = 2;
partition::PartitionTable& partitionTable = cuts->partitionTable;
// retrieve the current partitioning info
int numSubmaps = 2;
partition::PartitionTable& partitionTable = cuts->partitionTable;
// split the factors between child cliques and the current clique
vector<GenericGraph> frontalFactors; vector<GenericUnaryGraph> frontalUnaryFactors; typename SubNLG::Weeklinks weeklinks;
vector<size_t> localFrontals; vector<vector<size_t> > childFrontals, childSeps;
partitionFactorsAndVariables(gfg, unaryFactors, frontals, partitionTable, numSubmaps,
frontalFactors, frontalUnaryFactors, sepFactors, childFrontals, childSeps, localFrontals, weeklinks);
// split the factors between child cliques and the current clique
vector<GenericGraph> frontalFactors; vector<GenericUnaryGraph> frontalUnaryFactors; typename SubNLG::Weeklinks weeklinks;
vector<size_t> localFrontals; vector<vector<size_t> > childFrontals, childSeps;
partitionFactorsAndVariables(gfg, unaryFactors, frontals, partitionTable, numSubmaps,
frontalFactors, frontalUnaryFactors, sepFactors, childFrontals, childSeps, localFrontals, weeklinks);
// make a new cluster
boost::shared_ptr<SubNLG> current = makeSubNLG(sepFactors, localFrontals, sep, parent);
current->setWeeklinks(weeklinks);
// make a new cluster
boost::shared_ptr<SubNLG> current = makeSubNLG(sepFactors, localFrontals, sep, parent);
current->setWeeklinks(weeklinks);
// create child clusters
for (int i=0; i<2; i++) {
boost::shared_ptr<SubNLG> child = recursivePartition(frontalFactors[i], frontalUnaryFactors[i], childFrontals[i], childSeps[i],
cuts->children.empty() ? boost::shared_ptr<Cuts>() : cuts->children[i], current, workspace, verbose);
current->addChild(child);
}
return current;
}
// create child clusters
for (int i=0; i<2; i++) {
boost::shared_ptr<SubNLG> child = recursivePartition(frontalFactors[i], frontalUnaryFactors[i], childFrontals[i], childSeps[i],
cuts->children.empty() ? boost::shared_ptr<Cuts>() : cuts->children[i], current, workspace, verbose);
current->addChild(child);
}
return current;
}
}} //namespace

80
gtsam_unstable/partition/NestedDissection.h
View File

@ -14,56 +14,56 @@
namespace gtsam { namespace partition {
/**
* Apply nested dissection algorithm to nonlinear factor graphs
*/
template <class NLG, class SubNLG, class GenericGraph>
class NestedDissection {
public:
typedef boost::shared_ptr<SubNLG> sharedSubNLG;
/**
* Apply nested dissection algorithm to nonlinear factor graphs
*/
template <class NLG, class SubNLG, class GenericGraph>
class NestedDissection {
public:
typedef boost::shared_ptr<SubNLG> sharedSubNLG;
private:
NLG fg_; // the original nonlinear factor graph
Ordering ordering_; // the variable ordering in the nonlinear factor graph
std::vector<Symbol> int2symbol_; // the mapping from integer key to symbol
sharedSubNLG root_; // the root of generated cluster tree
private:
NLG fg_; // the original nonlinear factor graph
Ordering ordering_; // the variable ordering in the nonlinear factor graph
std::vector<Symbol> int2symbol_; // the mapping from integer key to symbol
sharedSubNLG root_; // the root of generated cluster tree
public:
sharedSubNLG root() const { return root_; }
public:
sharedSubNLG root() const { return root_; }
public:
/* constructor with post-determined partitoning*/
NestedDissection(const NLG& fg, const Ordering& ordering, const int numNodeStopPartition, const int minNodesPerMap, const bool verbose = false);
public:
/* constructor with post-determined partitoning*/
NestedDissection(const NLG& fg, const Ordering& ordering, const int numNodeStopPartition, const int minNodesPerMap, const bool verbose = false);
/* constructor with pre-determined cuts*/
NestedDissection(const NLG& fg, const Ordering& ordering, const boost::shared_ptr<Cuts>& cuts, const bool verbose = false);
/* constructor with pre-determined cuts*/
NestedDissection(const NLG& fg, const Ordering& ordering, const boost::shared_ptr<Cuts>& cuts, const bool verbose = false);
private:
/* convert generic subgraph to nonlinear subgraph */
sharedSubNLG makeSubNLG(const NLG& fg, const std::vector<size_t>& frontals, const std::vector<size_t>& sep, const boost::shared_ptr<SubNLG>& parent) const;
private:
/* convert generic subgraph to nonlinear subgraph */
sharedSubNLG makeSubNLG(const NLG& fg, const std::vector<size_t>& frontals, const std::vector<size_t>& sep, const boost::shared_ptr<SubNLG>& parent) const;
void processFactor(const typename GenericGraph::value_type& factor, const std::vector<int>& partitionTable, // input
std::vector<GenericGraph>& frontalFactors, NLG& sepFactors, std::vector<std::set<size_t> >& childSeps, // output factor graphs
typename SubNLG::Weeklinks& weeklinks) const;
void processFactor(const typename GenericGraph::value_type& factor, const std::vector<int>& partitionTable, // input
std::vector<GenericGraph>& frontalFactors, NLG& sepFactors, std::vector<std::set<size_t> >& childSeps, // output factor graphs
typename SubNLG::Weeklinks& weeklinks) const;
/* recursively partition the generic graph */
void partitionFactorsAndVariables(
const GenericGraph& fg, const GenericUnaryGraph& unaryFactors,
const std::vector<size_t>& keys, const std::vector<int>& partitionTable, const int numSubmaps, // input
std::vector<GenericGraph>& frontalFactors, vector<GenericUnaryGraph>& frontalUnaryFactors, NLG& sepFactors, // output factor graphs
std::vector<std::vector<size_t> >& childFrontals, std::vector<std::vector<size_t> >& childSeps, std::vector<size_t>& localFrontals, // output sub-orderings
typename SubNLG::Weeklinks& weeklinks) const;
/* recursively partition the generic graph */
void partitionFactorsAndVariables(
const GenericGraph& fg, const GenericUnaryGraph& unaryFactors,
const std::vector<size_t>& keys, const std::vector<int>& partitionTable, const int numSubmaps, // input
std::vector<GenericGraph>& frontalFactors, vector<GenericUnaryGraph>& frontalUnaryFactors, NLG& sepFactors, // output factor graphs
std::vector<std::vector<size_t> >& childFrontals, std::vector<std::vector<size_t> >& childSeps, std::vector<size_t>& localFrontals, // output sub-orderings
typename SubNLG::Weeklinks& weeklinks) const;
NLG collectOriginalFactors(const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors) const;
NLG collectOriginalFactors(const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors) const;
/* recursively partition the generic graph */
sharedSubNLG recursivePartition(const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors, const std::vector<size_t>& frontals, const std::vector<size_t>& sep,
const int numNodeStopPartition, const int minNodesPerMap, const boost::shared_ptr<SubNLG>& parent, WorkSpace& workspace, const bool verbose) const;
/* recursively partition the generic graph */
sharedSubNLG recursivePartition(const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors, const std::vector<size_t>& frontals, const std::vector<size_t>& sep,
const int numNodeStopPartition, const int minNodesPerMap, const boost::shared_ptr<SubNLG>& parent, WorkSpace& workspace, const bool verbose) const;
/* recursively partition the generic graph */
sharedSubNLG recursivePartition(const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors, const std::vector<size_t>& frontals, const std::vector<size_t>& sep,
const boost::shared_ptr<Cuts>& cuts, const boost::shared_ptr<SubNLG>& parent, WorkSpace& workspace, const bool verbose) const;
/* recursively partition the generic graph */
sharedSubNLG recursivePartition(const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors, const std::vector<size_t>& frontals, const std::vector<size_t>& sep,
const boost::shared_ptr<Cuts>& cuts, const boost::shared_ptr<SubNLG>& parent, WorkSpace& workspace, const bool verbose) const;
};
};
}} //namespace

44
gtsam_unstable/partition/PartitionWorkSpace.h
View File

@ -13,32 +13,32 @@
namespace gtsam { namespace partition {
typedef std::vector<int> PartitionTable;
typedef std::vector<int> PartitionTable;
// the work space, preallocated memory
struct WorkSpace {
std::vector<int> dictionary; // a mapping from the integer key in the original graph to 0-based index in the subgraph, useful when handling a subset of keys and graphs
boost::shared_ptr<std::vector<size_t> > dsf; // a block memory pre-allocated for DSFVector
PartitionTable partitionTable; // a mapping from a key to the submap index, 0 means the separator, i means the ith submap
// the work space, preallocated memory
struct WorkSpace {
std::vector<int> dictionary; // a mapping from the integer key in the original graph to 0-based index in the subgraph, useful when handling a subset of keys and graphs
boost::shared_ptr<std::vector<size_t> > dsf; // a block memory pre-allocated for DSFVector
PartitionTable partitionTable; // a mapping from a key to the submap index, 0 means the separator, i means the ith submap
// constructor
WorkSpace(const size_t numNodes) : dictionary(numNodes,0),
dsf(new std::vector<size_t>(numNodes, 0)), partitionTable(numNodes, -1) { }
// constructor
WorkSpace(const size_t numNodes) : dictionary(numNodes,0),
dsf(new std::vector<size_t>(numNodes, 0)), partitionTable(numNodes, -1) { }
// set up dictionary: -1: no such key, none-zero: the corresponding 0-based index
inline void prepareDictionary(const std::vector<size_t>& keys) {
int index = 0;
std::fill(dictionary.begin(), dictionary.end(), -1);
std::vector<size_t>::const_iterator it=keys.begin(), itLast=keys.end();
while(it!=itLast) dictionary[*(it++)] = index++;
}
};
// set up dictionary: -1: no such key, none-zero: the corresponding 0-based index
inline void prepareDictionary(const std::vector<size_t>& keys) {
int index = 0;
std::fill(dictionary.begin(), dictionary.end(), -1);
std::vector<size_t>::const_iterator it=keys.begin(), itLast=keys.end();
while(it!=itLast) dictionary[*(it++)] = index++;
}
};
// manually defined cuts
struct Cuts {
PartitionTable partitionTable;
std::vector<boost::shared_ptr<Cuts> > children;
};
// manually defined cuts
struct Cuts {
PartitionTable partitionTable;
std::vector<boost::shared_ptr<Cuts> > children;
};
}} // namespace

70
gtsam_unstable/partition/tests/testFindSeparator.cpp
View File

@ -185,45 +185,45 @@ TEST ( Partition, findSeparator2 )
// x25 x26 x27 x28
TEST ( Partition, findSeparator3_with_reduced_camera )
{
GenericGraph3D graph;
for (int j=1; j<=8; j++)
graph.push_back(boost::make_shared<GenericFactor3D>(25, j));
for (int j=1; j<=16; j++)
graph.push_back(boost::make_shared<GenericFactor3D>(26, j));
for (int j=9; j<=24; j++)
graph.push_back(boost::make_shared<GenericFactor3D>(27, j));
for (int j=17; j<=24; j++)
graph.push_back(boost::make_shared<GenericFactor3D>(28, j));
GenericGraph3D graph;
for (int j=1; j<=8; j++)
graph.push_back(boost::make_shared<GenericFactor3D>(25, j));
for (int j=1; j<=16; j++)
graph.push_back(boost::make_shared<GenericFactor3D>(26, j));
for (int j=9; j<=24; j++)
graph.push_back(boost::make_shared<GenericFactor3D>(27, j));
for (int j=17; j<=24; j++)
graph.push_back(boost::make_shared<GenericFactor3D>(28, j));
std::vector<size_t> keys;
for(int i=1; i<=28; i++)
keys.push_back(i);
std::vector<size_t> keys;
for(int i=1; i<=28; i++)
keys.push_back(i);
vector<Symbol> int2symbol;
int2symbol.push_back(Symbol('x',0)); // dummy
for(int i=1; i<=24; i++)
int2symbol.push_back(Symbol('l',i));
int2symbol.push_back(Symbol('x',25));
int2symbol.push_back(Symbol('x',26));
int2symbol.push_back(Symbol('x',27));
int2symbol.push_back(Symbol('x',28));
vector<Symbol> int2symbol;
int2symbol.push_back(Symbol('x',0)); // dummy
for(int i=1; i<=24; i++)
int2symbol.push_back(Symbol('l',i));
int2symbol.push_back(Symbol('x',25));
int2symbol.push_back(Symbol('x',26));
int2symbol.push_back(Symbol('x',27));
int2symbol.push_back(Symbol('x',28));
WorkSpace workspace(29);
bool reduceGraph = true;
int numIsland = findSeparator(graph, keys, 3, workspace, false, int2symbol, reduceGraph, 0, 0);
LONGS_EQUAL(2, numIsland);
WorkSpace workspace(29);
bool reduceGraph = true;
int numIsland = findSeparator(graph, keys, 3, workspace, false, int2symbol, reduceGraph, 0, 0);
LONGS_EQUAL(2, numIsland);
partition::PartitionTable& partitionTable = workspace.partitionTable;
for (int j=1; j<=8; j++)
LONGS_EQUAL(1, partitionTable[j]);
for (int j=9; j<=16; j++)
LONGS_EQUAL(0, partitionTable[j]);
for (int j=17; j<=24; j++)
LONGS_EQUAL(2, partitionTable[j]);
LONGS_EQUAL(1, partitionTable[25]);
LONGS_EQUAL(1, partitionTable[26]);
LONGS_EQUAL(2, partitionTable[27]);
LONGS_EQUAL(2, partitionTable[28]);
partition::PartitionTable& partitionTable = workspace.partitionTable;
for (int j=1; j<=8; j++)
LONGS_EQUAL(1, partitionTable[j]);
for (int j=9; j<=16; j++)
LONGS_EQUAL(0, partitionTable[j]);
for (int j=17; j<=24; j++)
LONGS_EQUAL(2, partitionTable[j]);
LONGS_EQUAL(1, partitionTable[25]);
LONGS_EQUAL(1, partitionTable[26]);
LONGS_EQUAL(2, partitionTable[27]);
LONGS_EQUAL(2, partitionTable[28]);
}
/* ************************************************************************* */

270
gtsam_unstable/partition/tests/testGenericGraph.cpp
View File

@ -29,29 +29,29 @@ using namespace gtsam::partition;
*/
TEST ( GenerciGraph, findIslands )
{
GenericGraph2D graph;
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 9, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(5, NODE_POSE_2D, 9, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(6, NODE_POSE_2D, 9, NODE_LANDMARK_2D));
GenericGraph2D graph;
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 9, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(5, NODE_POSE_2D, 9, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(6, NODE_POSE_2D, 9, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 2, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 3, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 5, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(5, NODE_POSE_2D, 6, NODE_POSE_2D));
std::vector<size_t> keys; keys += 1, 2, 3, 4, 5, 6, 7, 8, 9;
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 2, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 3, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 5, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(5, NODE_POSE_2D, 6, NODE_POSE_2D));
std::vector<size_t> keys; keys += 1, 2, 3, 4, 5, 6, 7, 8, 9;
WorkSpace workspace(10); // from 0 to 9
list<vector<size_t> > islands = findIslands(graph, keys, workspace, 7, 2);
LONGS_EQUAL(2, islands.size());
vector<size_t> island1; island1 += 1, 2, 3, 7, 8;
vector<size_t> island2; island2 += 4, 5, 6, 9;
CHECK(island1 == islands.front());
CHECK(island2 == islands.back());
WorkSpace workspace(10); // from 0 to 9
list<vector<size_t> > islands = findIslands(graph, keys, workspace, 7, 2);
LONGS_EQUAL(2, islands.size());
vector<size_t> island1; island1 += 1, 2, 3, 7, 8;
vector<size_t> island2; island2 += 4, 5, 6, 9;
CHECK(island1 == islands.front());
CHECK(island2 == islands.back());
}
/* ************************************************************************* */
@ -62,27 +62,27 @@ TEST ( GenerciGraph, findIslands )
*/
TEST( GenerciGraph, findIslands2 )
{
GenericGraph2D graph;
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(5, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(6, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
GenericGraph2D graph;
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(5, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(6, NODE_POSE_2D, 8, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 2, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 3, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 5, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(5, NODE_POSE_2D, 6, NODE_POSE_2D));
std::vector<size_t> keys; keys += 1, 2, 3, 4, 5, 6, 7, 8;
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 2, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 3, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 5, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(5, NODE_POSE_2D, 6, NODE_POSE_2D));
std::vector<size_t> keys; keys += 1, 2, 3, 4, 5, 6, 7, 8;
WorkSpace workspace(15); // from 0 to 8, but testing over-allocation here
list<vector<size_t> > islands = findIslands(graph, keys, workspace, 7, 2);
LONGS_EQUAL(1, islands.size());
vector<size_t> island1; island1 += 1, 2, 3, 4, 5, 6, 7, 8;
CHECK(island1 == islands.front());
WorkSpace workspace(15); // from 0 to 8, but testing over-allocation here
list<vector<size_t> > islands = findIslands(graph, keys, workspace, 7, 2);
LONGS_EQUAL(1, islands.size());
vector<size_t> island1; island1 += 1, 2, 3, 4, 5, 6, 7, 8;
CHECK(island1 == islands.front());
}
/* ************************************************************************* */
@ -92,21 +92,21 @@ TEST( GenerciGraph, findIslands2 )
*/
TEST ( GenerciGraph, findIslands3 )
{
GenericGraph2D graph;
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 5, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 6, NODE_LANDMARK_2D));
GenericGraph2D graph;
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 5, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 6, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 3, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 4, NODE_POSE_2D));
std::vector<size_t> keys; keys += 1, 2, 3, 4, 5, 6;
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 3, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 4, NODE_POSE_2D));
std::vector<size_t> keys; keys += 1, 2, 3, 4, 5, 6;
WorkSpace workspace(7); // from 0 to 9
list<vector<size_t> > islands = findIslands(graph, keys, workspace, 7, 2);
LONGS_EQUAL(2, islands.size());
vector<size_t> island1; island1 += 1, 5;
vector<size_t> island2; island2 += 2, 3, 4, 6;
CHECK(island1 == islands.front());
CHECK(island2 == islands.back());
WorkSpace workspace(7); // from 0 to 9
list<vector<size_t> > islands = findIslands(graph, keys, workspace, 7, 2);
LONGS_EQUAL(2, islands.size());
vector<size_t> island1; island1 += 1, 5;
vector<size_t> island2; island2 += 2, 3, 4, 6;
CHECK(island1 == islands.front());
CHECK(island2 == islands.back());
}
/* ************************************************************************* */
@ -115,18 +115,18 @@ TEST ( GenerciGraph, findIslands3 )
*/
TEST ( GenerciGraph, findIslands4 )
{
GenericGraph2D graph;
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 4, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(7, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
std::vector<size_t> keys; keys += 3, 4, 7;
GenericGraph2D graph;
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 4, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(7, NODE_POSE_2D, 7, NODE_LANDMARK_2D));
std::vector<size_t> keys; keys += 3, 4, 7;
WorkSpace workspace(8); // from 0 to 7
list<vector<size_t> > islands = findIslands(graph, keys, workspace, 7, 2);
LONGS_EQUAL(2, islands.size());
vector<size_t> island1; island1 += 3, 4;
vector<size_t> island2; island2 += 7;
CHECK(island1 == islands.front());
CHECK(island2 == islands.back());
WorkSpace workspace(8); // from 0 to 7
list<vector<size_t> > islands = findIslands(graph, keys, workspace, 7, 2);
LONGS_EQUAL(2, islands.size());
vector<size_t> island1; island1 += 3, 4;
vector<size_t> island2; island2 += 7;
CHECK(island1 == islands.front());
CHECK(island2 == islands.back());
}
/* ************************************************************************* */
@ -137,24 +137,24 @@ TEST ( GenerciGraph, findIslands4 )
*/
TEST ( GenerciGraph, findIslands5 )
{
GenericGraph2D graph;
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 5, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 5, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 5, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 5, NODE_LANDMARK_2D));
GenericGraph2D graph;
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 5, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 5, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(3, NODE_POSE_2D, 5, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(4, NODE_POSE_2D, 5, NODE_LANDMARK_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 3, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 4, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(1, NODE_POSE_2D, 3, NODE_POSE_2D));
graph.push_back(boost::make_shared<GenericFactor2D>(2, NODE_POSE_2D, 4, NODE_POSE_2D));
std::vector<size_t> keys; keys += 1, 2, 3, 4, 5;
std::vector<size_t> keys; keys += 1, 2, 3, 4, 5;
WorkSpace workspace(6); // from 0 to 5
list<vector<size_t> > islands = findIslands(graph, keys, workspace, 7, 2);
LONGS_EQUAL(2, islands.size());
vector<size_t> island1; island1 += 1, 3, 5;
vector<size_t> island2; island2 += 2, 4;
CHECK(island1 == islands.front());
CHECK(island2 == islands.back());
WorkSpace workspace(6); // from 0 to 5
list<vector<size_t> > islands = findIslands(graph, keys, workspace, 7, 2);
LONGS_EQUAL(2, islands.size());
vector<size_t> island1; island1 += 1, 3, 5;
vector<size_t> island2; island2 += 2, 4;
CHECK(island1 == islands.front());
CHECK(island2 == islands.back());
}
/* ************************************************************************* */
@ -165,31 +165,31 @@ TEST ( GenerciGraph, findIslands5 )
*/
TEST ( GenerciGraph, reduceGenericGraph )
{
GenericGraph3D graph;
graph.push_back(boost::make_shared<GenericFactor3D>(1, 3));
graph.push_back(boost::make_shared<GenericFactor3D>(1, 4));
graph.push_back(boost::make_shared<GenericFactor3D>(1, 5));
graph.push_back(boost::make_shared<GenericFactor3D>(2, 5));
graph.push_back(boost::make_shared<GenericFactor3D>(2, 6));
GenericGraph3D graph;
graph.push_back(boost::make_shared<GenericFactor3D>(1, 3));
graph.push_back(boost::make_shared<GenericFactor3D>(1, 4));
graph.push_back(boost::make_shared<GenericFactor3D>(1, 5));
graph.push_back(boost::make_shared<GenericFactor3D>(2, 5));
graph.push_back(boost::make_shared<GenericFactor3D>(2, 6));
std::vector<size_t> cameraKeys, landmarkKeys;
cameraKeys.push_back(1);
cameraKeys.push_back(2);
landmarkKeys.push_back(3);
landmarkKeys.push_back(4);
landmarkKeys.push_back(5);
landmarkKeys.push_back(6);
std::vector<size_t> cameraKeys, landmarkKeys;
cameraKeys.push_back(1);
cameraKeys.push_back(2);
landmarkKeys.push_back(3);
landmarkKeys.push_back(4);
landmarkKeys.push_back(5);
landmarkKeys.push_back(6);
std::vector<int> dictionary;
dictionary.resize(7, -1); // from 0 to 6
dictionary[1] = 0;
dictionary[2] = 1;
std::vector<int> dictionary;
dictionary.resize(7, -1); // from 0 to 6
dictionary[1] = 0;
dictionary[2] = 1;
GenericGraph3D reduced;
std::map<size_t, vector<size_t> > cameraToLandmarks;
reduceGenericGraph(graph, cameraKeys, landmarkKeys, dictionary, reduced);
LONGS_EQUAL(1, reduced.size());
LONGS_EQUAL(1, reduced[0]->key1.index); LONGS_EQUAL(2, reduced[0]->key2.index);
GenericGraph3D reduced;
std::map<size_t, vector<size_t> > cameraToLandmarks;
reduceGenericGraph(graph, cameraKeys, landmarkKeys, dictionary, reduced);
LONGS_EQUAL(1, reduced.size());
LONGS_EQUAL(1, reduced[0]->key1.index); LONGS_EQUAL(2, reduced[0]->key2.index);
}
/* ************************************************************************* */
@ -200,53 +200,53 @@ TEST ( GenerciGraph, reduceGenericGraph )
*/
TEST ( GenericGraph, reduceGenericGraph2 )
{
GenericGraph3D graph;
graph.push_back(boost::make_shared<GenericFactor3D>(1, 3, 0, NODE_POSE_3D, NODE_LANDMARK_3D));
graph.push_back(boost::make_shared<GenericFactor3D>(1, 4, 1, NODE_POSE_3D, NODE_LANDMARK_3D));
graph.push_back(boost::make_shared<GenericFactor3D>(1, 5, 2, NODE_POSE_3D, NODE_LANDMARK_3D));
graph.push_back(boost::make_shared<GenericFactor3D>(2, 5, 3, NODE_POSE_3D, NODE_LANDMARK_3D));
graph.push_back(boost::make_shared<GenericFactor3D>(2, 6, 4, NODE_POSE_3D, NODE_LANDMARK_3D));
graph.push_back(boost::make_shared<GenericFactor3D>(2, 7, 5, NODE_POSE_3D, NODE_POSE_3D));
GenericGraph3D graph;
graph.push_back(boost::make_shared<GenericFactor3D>(1, 3, 0, NODE_POSE_3D, NODE_LANDMARK_3D));
graph.push_back(boost::make_shared<GenericFactor3D>(1, 4, 1, NODE_POSE_3D, NODE_LANDMARK_3D));
graph.push_back(boost::make_shared<GenericFactor3D>(1, 5, 2, NODE_POSE_3D, NODE_LANDMARK_3D));
graph.push_back(boost::make_shared<GenericFactor3D>(2, 5, 3, NODE_POSE_3D, NODE_LANDMARK_3D));
graph.push_back(boost::make_shared<GenericFactor3D>(2, 6, 4, NODE_POSE_3D, NODE_LANDMARK_3D));
graph.push_back(boost::make_shared<GenericFactor3D>(2, 7, 5, NODE_POSE_3D, NODE_POSE_3D));
std::vector<size_t> cameraKeys, landmarkKeys;
cameraKeys.push_back(1);
cameraKeys.push_back(2);
cameraKeys.push_back(7);
landmarkKeys.push_back(3);
landmarkKeys.push_back(4);
landmarkKeys.push_back(5);
landmarkKeys.push_back(6);
std::vector<size_t> cameraKeys, landmarkKeys;
cameraKeys.push_back(1);
cameraKeys.push_back(2);
cameraKeys.push_back(7);
landmarkKeys.push_back(3);
landmarkKeys.push_back(4);
landmarkKeys.push_back(5);
landmarkKeys.push_back(6);
std::vector<int> dictionary;
dictionary.resize(8, -1); // from 0 to 7
dictionary[1] = 0;
dictionary[2] = 1;
dictionary[7] = 6;
std::vector<int> dictionary;
dictionary.resize(8, -1); // from 0 to 7
dictionary[1] = 0;
dictionary[2] = 1;
dictionary[7] = 6;
GenericGraph3D reduced;
std::map<size_t, vector<size_t> > cameraToLandmarks;
reduceGenericGraph(graph, cameraKeys, landmarkKeys, dictionary, reduced);
LONGS_EQUAL(2, reduced.size());
LONGS_EQUAL(1, reduced[0]->key1.index); LONGS_EQUAL(2, reduced[0]->key2.index);
LONGS_EQUAL(2, reduced[1]->key1.index); LONGS_EQUAL(7, reduced[1]->key2.index);
GenericGraph3D reduced;
std::map<size_t, vector<size_t> > cameraToLandmarks;
reduceGenericGraph(graph, cameraKeys, landmarkKeys, dictionary, reduced);
LONGS_EQUAL(2, reduced.size());
LONGS_EQUAL(1, reduced[0]->key1.index); LONGS_EQUAL(2, reduced[0]->key2.index);
LONGS_EQUAL(2, reduced[1]->key1.index); LONGS_EQUAL(7, reduced[1]->key2.index);
}
/* ************************************************************************* */
TEST ( GenerciGraph, hasCommonCamera )
{
std::set<size_t> cameras1; cameras1 += 1, 2, 3, 4, 5;
std::set<size_t> cameras2; cameras2 += 8, 7, 6, 5;
bool actual = hasCommonCamera(cameras1, cameras2);
CHECK(actual);
std::set<size_t> cameras1; cameras1 += 1, 2, 3, 4, 5;
std::set<size_t> cameras2; cameras2 += 8, 7, 6, 5;
bool actual = hasCommonCamera(cameras1, cameras2);
CHECK(actual);
}
/* ************************************************************************* */
TEST ( GenerciGraph, hasCommonCamera2 )
{
std::set<size_t> cameras1; cameras1 += 1, 3, 5, 7;
std::set<size_t> cameras2; cameras2 += 2, 4, 6, 8, 10;
bool actual = hasCommonCamera(cameras1, cameras2);
CHECK(!actual);
std::set<size_t> cameras1; cameras1 += 1, 3, 5, 7;
std::set<size_t> cameras2; cameras2 += 2, 4, 6, 8, 10;
bool actual = hasCommonCamera(cameras1, cameras2);
CHECK(!actual);
}
/* ************************************************************************* */

434
gtsam_unstable/partition/tests/testNestedDissection.cpp
View File

@ -32,22 +32,22 @@ using namespace gtsam::partition;
// l1
TEST ( NestedDissection, oneIsland )
{
using namespace submapPlanarSLAM;
typedef TSAM2D::SubNLG SubNLG;
Graph fg;
fg.addOdometry(1, 2, Pose2(), odoNoise);
fg.addBearingRange(1, 1, Rot2(), 0., bearingRangeNoise);
fg.addBearingRange(2, 1, Rot2(), 0., bearingRangeNoise);
fg.addPoseConstraint(1, Pose2());
using namespace submapPlanarSLAM;
typedef TSAM2D::SubNLG SubNLG;
Graph fg;
fg.addOdometry(1, 2, Pose2(), odoNoise);
fg.addBearingRange(1, 1, Rot2(), 0., bearingRangeNoise);
fg.addBearingRange(2, 1, Rot2(), 0., bearingRangeNoise);
fg.addPoseConstraint(1, Pose2());
Ordering ordering; ordering += x1, x2, l1;
Ordering ordering; ordering += x1, x2, l1;
int numNodeStopPartition = 1e3;
int minNodesPerMap = 1e3;
NestedDissection<Graph, SubNLG, GenericGraph2D> nd(fg, ordering, numNodeStopPartition, minNodesPerMap);
LONGS_EQUAL(4, nd.root()->size());
LONGS_EQUAL(3, nd.root()->frontal().size());
LONGS_EQUAL(0, nd.root()->children().size());
int numNodeStopPartition = 1e3;
int minNodesPerMap = 1e3;
NestedDissection<Graph, SubNLG, GenericGraph2D> nd(fg, ordering, numNodeStopPartition, minNodesPerMap);
LONGS_EQUAL(4, nd.root()->size());
LONGS_EQUAL(3, nd.root()->frontal().size());
LONGS_EQUAL(0, nd.root()->children().size());
}
/* ************************************************************************* */
@ -56,35 +56,35 @@ TEST ( NestedDissection, oneIsland )
// x2/ \x5
TEST ( NestedDissection, TwoIslands )
{
using namespace submapPlanarSLAM;
typedef TSAM2D::SubNLG SubNLG;
Graph fg;
fg.addOdometry(1, 2, Pose2(), odoNoise);
fg.addOdometry(1, 3, Pose2(), odoNoise);
fg.addOdometry(2, 3, Pose2(), odoNoise);
fg.addOdometry(3, 4, Pose2(), odoNoise);
fg.addOdometry(4, 5, Pose2(), odoNoise);
fg.addOdometry(3, 5, Pose2(), odoNoise);
fg.addPoseConstraint(1, Pose2());
fg.addPoseConstraint(4, Pose2());
Ordering ordering; ordering += x1, x2, x3, x4, x5;
using namespace submapPlanarSLAM;
typedef TSAM2D::SubNLG SubNLG;
Graph fg;
fg.addOdometry(1, 2, Pose2(), odoNoise);
fg.addOdometry(1, 3, Pose2(), odoNoise);
fg.addOdometry(2, 3, Pose2(), odoNoise);
fg.addOdometry(3, 4, Pose2(), odoNoise);
fg.addOdometry(4, 5, Pose2(), odoNoise);
fg.addOdometry(3, 5, Pose2(), odoNoise);
fg.addPoseConstraint(1, Pose2());
fg.addPoseConstraint(4, Pose2());
Ordering ordering; ordering += x1, x2, x3, x4, x5;
int numNodeStopPartition = 2;
int minNodesPerMap = 1;
NestedDissection<Graph, SubNLG, GenericGraph2D> nd(fg, ordering, numNodeStopPartition, minNodesPerMap);
// root submap
LONGS_EQUAL(0, nd.root()->size());
LONGS_EQUAL(1, nd.root()->frontal().size());
LONGS_EQUAL(0, nd.root()->separator().size());
LONGS_EQUAL(2, nd.root()->children().size()); // 2 leaf submaps
int numNodeStopPartition = 2;
int minNodesPerMap = 1;
NestedDissection<Graph, SubNLG, GenericGraph2D> nd(fg, ordering, numNodeStopPartition, minNodesPerMap);
// root submap
LONGS_EQUAL(0, nd.root()->size());
LONGS_EQUAL(1, nd.root()->frontal().size());
LONGS_EQUAL(0, nd.root()->separator().size());
LONGS_EQUAL(2, nd.root()->children().size()); // 2 leaf submaps
// the 1st submap
LONGS_EQUAL(2, nd.root()->children()[0]->frontal().size());
LONGS_EQUAL(4, nd.root()->children()[0]->size());
// the 1st submap
LONGS_EQUAL(2, nd.root()->children()[0]->frontal().size());
LONGS_EQUAL(4, nd.root()->children()[0]->size());
// the 2nd submap
LONGS_EQUAL(2, nd.root()->children()[1]->frontal().size());
LONGS_EQUAL(4, nd.root()->children()[1]->size());
// the 2nd submap
LONGS_EQUAL(2, nd.root()->children()[1]->frontal().size());
LONGS_EQUAL(4, nd.root()->children()[1]->size());
}
/* ************************************************************************* */
@ -93,40 +93,40 @@ TEST ( NestedDissection, TwoIslands )
// x2/ \x5
TEST ( NestedDissection, FourIslands )
{
using namespace submapPlanarSLAM;
typedef TSAM2D::SubNLG SubNLG;
Graph fg;
fg.addOdometry(1, 3, Pose2(), odoNoise);
fg.addOdometry(2, 3, Pose2(), odoNoise);
fg.addOdometry(3, 4, Pose2(), odoNoise);
fg.addOdometry(3, 5, Pose2(), odoNoise);
fg.addPoseConstraint(1, Pose2());
fg.addPoseConstraint(4, Pose2());
Ordering ordering; ordering += x1, x2, x3, x4, x5;
using namespace submapPlanarSLAM;
typedef TSAM2D::SubNLG SubNLG;
Graph fg;
fg.addOdometry(1, 3, Pose2(), odoNoise);
fg.addOdometry(2, 3, Pose2(), odoNoise);
fg.addOdometry(3, 4, Pose2(), odoNoise);
fg.addOdometry(3, 5, Pose2(), odoNoise);
fg.addPoseConstraint(1, Pose2());
fg.addPoseConstraint(4, Pose2());
Ordering ordering; ordering += x1, x2, x3, x4, x5;
int numNodeStopPartition = 2;
int minNodesPerMap = 1;
NestedDissection<Graph, SubNLG, GenericGraph2D> nd(fg, ordering, numNodeStopPartition, minNodesPerMap);
LONGS_EQUAL(0, nd.root()->size());
LONGS_EQUAL(1, nd.root()->frontal().size());
LONGS_EQUAL(0, nd.root()->separator().size());
LONGS_EQUAL(4, nd.root()->children().size()); // 4 leaf submaps
int numNodeStopPartition = 2;
int minNodesPerMap = 1;
NestedDissection<Graph, SubNLG, GenericGraph2D> nd(fg, ordering, numNodeStopPartition, minNodesPerMap);
LONGS_EQUAL(0, nd.root()->size());
LONGS_EQUAL(1, nd.root()->frontal().size());
LONGS_EQUAL(0, nd.root()->separator().size());
LONGS_EQUAL(4, nd.root()->children().size()); // 4 leaf submaps
// the 1st submap
LONGS_EQUAL(1, nd.root()->children()[0]->frontal().size());
LONGS_EQUAL(2, nd.root()->children()[0]->size());
// the 1st submap
LONGS_EQUAL(1, nd.root()->children()[0]->frontal().size());
LONGS_EQUAL(2, nd.root()->children()[0]->size());
// the 2nd submap
LONGS_EQUAL(1, nd.root()->children()[1]->frontal().size());
LONGS_EQUAL(2, nd.root()->children()[1]->size());
// the 2nd submap
LONGS_EQUAL(1, nd.root()->children()[1]->frontal().size());
LONGS_EQUAL(2, nd.root()->children()[1]->size());
// the 3rd submap
LONGS_EQUAL(1, nd.root()->children()[2]->frontal().size());
LONGS_EQUAL(1, nd.root()->children()[2]->size());
// the 3rd submap
LONGS_EQUAL(1, nd.root()->children()[2]->frontal().size());
LONGS_EQUAL(1, nd.root()->children()[2]->size());
// the 4th submap
LONGS_EQUAL(1, nd.root()->children()[3]->frontal().size());
LONGS_EQUAL(1, nd.root()->children()[3]->size());
// the 4th submap
LONGS_EQUAL(1, nd.root()->children()[3]->frontal().size());
LONGS_EQUAL(1, nd.root()->children()[3]->size());
}
/* ************************************************************************* */
@ -137,41 +137,41 @@ TEST ( NestedDissection, FourIslands )
// x5
TEST ( NestedDissection, weekLinks )
{
using namespace submapPlanarSLAM;
typedef TSAM2D::SubNLG SubNLG;
Graph fg;
fg.addOdometry(1, 2, Pose2(), odoNoise);
fg.addOdometry(2, 3, Pose2(), odoNoise);
fg.addOdometry(2, 4, Pose2(), odoNoise);
fg.addOdometry(3, 4, Pose2(), odoNoise);
fg.addBearingRange(1, 6, Rot2(), 0., bearingRangeNoise);
fg.addBearingRange(2, 6, Rot2(), 0., bearingRangeNoise);
fg.addBearingRange(5, 6, Rot2(), 0., bearingRangeNoise);
fg.addPoseConstraint(1, Pose2());
fg.addPoseConstraint(4, Pose2());
fg.addPoseConstraint(5, Pose2());
Ordering ordering; ordering += x1, x2, x3, x4, x5, l6;
using namespace submapPlanarSLAM;
typedef TSAM2D::SubNLG SubNLG;
Graph fg;
fg.addOdometry(1, 2, Pose2(), odoNoise);
fg.addOdometry(2, 3, Pose2(), odoNoise);
fg.addOdometry(2, 4, Pose2(), odoNoise);
fg.addOdometry(3, 4, Pose2(), odoNoise);
fg.addBearingRange(1, 6, Rot2(), 0., bearingRangeNoise);
fg.addBearingRange(2, 6, Rot2(), 0., bearingRangeNoise);
fg.addBearingRange(5, 6, Rot2(), 0., bearingRangeNoise);
fg.addPoseConstraint(1, Pose2());
fg.addPoseConstraint(4, Pose2());
fg.addPoseConstraint(5, Pose2());
Ordering ordering; ordering += x1, x2, x3, x4, x5, l6;
int numNodeStopPartition = 2;
int minNodesPerMap = 1;
NestedDissection<Graph, SubNLG, GenericGraph2D> nd(fg, ordering, numNodeStopPartition, minNodesPerMap);
LONGS_EQUAL(0, nd.root()->size()); // one weeklink
LONGS_EQUAL(1, nd.root()->frontal().size());
LONGS_EQUAL(0, nd.root()->separator().size());
LONGS_EQUAL(3, nd.root()->children().size()); // 4 leaf submaps
LONGS_EQUAL(1, nd.root()->weeklinks().size());
int numNodeStopPartition = 2;
int minNodesPerMap = 1;
NestedDissection<Graph, SubNLG, GenericGraph2D> nd(fg, ordering, numNodeStopPartition, minNodesPerMap);
LONGS_EQUAL(0, nd.root()->size()); // one weeklink
LONGS_EQUAL(1, nd.root()->frontal().size());
LONGS_EQUAL(0, nd.root()->separator().size());
LONGS_EQUAL(3, nd.root()->children().size()); // 4 leaf submaps
LONGS_EQUAL(1, nd.root()->weeklinks().size());
// the 1st submap
LONGS_EQUAL(2, nd.root()->children()[0]->frontal().size()); // x3 and x4
LONGS_EQUAL(4, nd.root()->children()[0]->size());
// the 1st submap
LONGS_EQUAL(2, nd.root()->children()[0]->frontal().size()); // x3 and x4
LONGS_EQUAL(4, nd.root()->children()[0]->size());
// the 2nd submap
LONGS_EQUAL(2, nd.root()->children()[1]->frontal().size()); // x1 and l6
LONGS_EQUAL(4, nd.root()->children()[1]->size());
//
// the 3rd submap
LONGS_EQUAL(1, nd.root()->children()[2]->frontal().size()); // x5
LONGS_EQUAL(1, nd.root()->children()[2]->size());
// the 2nd submap
LONGS_EQUAL(2, nd.root()->children()[1]->frontal().size()); // x1 and l6
LONGS_EQUAL(4, nd.root()->children()[1]->size());
//
// the 3rd submap
LONGS_EQUAL(1, nd.root()->children()[2]->frontal().size()); // x5
LONGS_EQUAL(1, nd.root()->children()[2]->size());
}
/* ************************************************************************* */
@ -184,86 +184,86 @@ TEST ( NestedDissection, weekLinks )
*/
TEST ( NestedDissection, manual_cuts )
{
using namespace submapPlanarSLAM;
typedef partition::Cuts Cuts;
typedef TSAM2D::SubNLG SubNLG;
typedef partition::PartitionTable PartitionTable;
Graph fg;
fg.addOdometry(x0, x1, Pose2(1.0, 0, 0), odoNoise);
fg.addOdometry(x1, x2, Pose2(1.0, 0, 0), odoNoise);
using namespace submapPlanarSLAM;
typedef partition::Cuts Cuts;
typedef TSAM2D::SubNLG SubNLG;
typedef partition::PartitionTable PartitionTable;
Graph fg;
fg.addOdometry(x0, x1, Pose2(1.0, 0, 0), odoNoise);
fg.addOdometry(x1, x2, Pose2(1.0, 0, 0), odoNoise);
fg.addBearingRange(x0, l1, Rot2::fromAngle( M_PI_2), 1, bearingRangeNoise);
fg.addBearingRange(x0, l4, Rot2::fromAngle(-M_PI_2), 1, bearingRangeNoise);
fg.addBearingRange(x0, l2, Rot2::fromAngle( M_PI_4), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x0, l5, Rot2::fromAngle(-M_PI_4), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x0, l1, Rot2::fromAngle( M_PI_2), 1, bearingRangeNoise);
fg.addBearingRange(x0, l4, Rot2::fromAngle(-M_PI_2), 1, bearingRangeNoise);
fg.addBearingRange(x0, l2, Rot2::fromAngle( M_PI_4), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x0, l5, Rot2::fromAngle(-M_PI_4), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x1, l1, Rot2::fromAngle( M_PI_4 * 3), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x1, l2, Rot2::fromAngle( M_PI_2), 1, bearingRangeNoise);
fg.addBearingRange(x1, l3, Rot2::fromAngle( M_PI_4), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x1, l4, Rot2::fromAngle(-M_PI_4 * 3), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x1, l5, Rot2::fromAngle( M_PI_2), 1, bearingRangeNoise);
fg.addBearingRange(x1, l6, Rot2::fromAngle(-M_PI_4), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x1, l1, Rot2::fromAngle( M_PI_4 * 3), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x1, l2, Rot2::fromAngle( M_PI_2), 1, bearingRangeNoise);
fg.addBearingRange(x1, l3, Rot2::fromAngle( M_PI_4), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x1, l4, Rot2::fromAngle(-M_PI_4 * 3), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x1, l5, Rot2::fromAngle( M_PI_2), 1, bearingRangeNoise);
fg.addBearingRange(x1, l6, Rot2::fromAngle(-M_PI_4), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x2, l2, Rot2::fromAngle( M_PI_4 * 3), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x2, l5, Rot2::fromAngle(-M_PI_4 * 3), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x2, l3, Rot2::fromAngle( M_PI_2), 1, bearingRangeNoise);
fg.addBearingRange(x2, l6, Rot2::fromAngle(-M_PI_2), 1, bearingRangeNoise);
fg.addBearingRange(x2, l2, Rot2::fromAngle( M_PI_4 * 3), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x2, l5, Rot2::fromAngle(-M_PI_4 * 3), sqrt(2), bearingRangeNoise);
fg.addBearingRange(x2, l3, Rot2::fromAngle( M_PI_2), 1, bearingRangeNoise);
fg.addBearingRange(x2, l6, Rot2::fromAngle(-M_PI_2), 1, bearingRangeNoise);
fg.addPrior(x0, Pose2(0.1, 0, 0), priorNoise);
fg.addPrior(x0, Pose2(0.1, 0, 0), priorNoise);
// generate ordering
Ordering ordering; ordering += x0, x1, x2, l1, l2, l3, l4, l5, l6;
// generate ordering
Ordering ordering; ordering += x0, x1, x2, l1, l2, l3, l4, l5, l6;
// define cuts
boost::shared_ptr<Cuts> cuts(new Cuts());
cuts->partitionTable = PartitionTable(9, -1); PartitionTable* p = &cuts->partitionTable;
//x0 x1 x2 l1 l2 l3 l4 l5 l6
(*p)[0]=1; (*p)[1]=0; (*p)[2]=2; (*p)[3]=1; (*p)[4]=0; (*p)[5]=2; (*p)[6]=1; (*p)[7]=0; (*p)[8]=2;
// define cuts
boost::shared_ptr<Cuts> cuts(new Cuts());
cuts->partitionTable = PartitionTable(9, -1); PartitionTable* p = &cuts->partitionTable;
//x0 x1 x2 l1 l2 l3 l4 l5 l6
(*p)[0]=1; (*p)[1]=0; (*p)[2]=2; (*p)[3]=1; (*p)[4]=0; (*p)[5]=2; (*p)[6]=1; (*p)[7]=0; (*p)[8]=2;
cuts->children.push_back(boost::shared_ptr<Cuts>(new Cuts()));
cuts->children[0]->partitionTable = PartitionTable(9, -1); p = &cuts->children[0]->partitionTable;
//x0 x1 x2 l1 l2 l3 l4 l5 l6
(*p)[0]=0; (*p)[1]=-1; (*p)[2]=-1; (*p)[3]=1; (*p)[4]=-1; (*p)[5]=-1; (*p)[6]=2; (*p)[7]=-1; (*p)[8]=-1;
cuts->children.push_back(boost::shared_ptr<Cuts>(new Cuts()));
cuts->children[0]->partitionTable = PartitionTable(9, -1); p = &cuts->children[0]->partitionTable;
//x0 x1 x2 l1 l2 l3 l4 l5 l6
(*p)[0]=0; (*p)[1]=-1; (*p)[2]=-1; (*p)[3]=1; (*p)[4]=-1; (*p)[5]=-1; (*p)[6]=2; (*p)[7]=-1; (*p)[8]=-1;
cuts->children.push_back(boost::shared_ptr<Cuts>(new Cuts()));
cuts->children[1]->partitionTable = PartitionTable(9, -1); p = &cuts->children[1]->partitionTable;
//x0 x1 x2 l1 l2 l3 l4 l5 l6
(*p)[0]=-1; (*p)[1]=-1; (*p)[2]=0; (*p)[3]=-1; (*p)[4]=-1; (*p)[5]=1; (*p)[6]=-1; (*p)[7]=-1; (*p)[8]=2;
cuts->children.push_back(boost::shared_ptr<Cuts>(new Cuts()));
cuts->children[1]->partitionTable = PartitionTable(9, -1); p = &cuts->children[1]->partitionTable;
//x0 x1 x2 l1 l2 l3 l4 l5 l6
(*p)[0]=-1; (*p)[1]=-1; (*p)[2]=0; (*p)[3]=-1; (*p)[4]=-1; (*p)[5]=1; (*p)[6]=-1; (*p)[7]=-1; (*p)[8]=2;
// nested dissection
NestedDissection<Graph, SubNLG, GenericGraph2D> nd(fg, ordering, cuts);
LONGS_EQUAL(2, nd.root()->size());
LONGS_EQUAL(3, nd.root()->frontal().size());
LONGS_EQUAL(0, nd.root()->separator().size());
LONGS_EQUAL(2, nd.root()->children().size()); // 2 leaf submaps
LONGS_EQUAL(0, nd.root()->weeklinks().size());
// nested dissection
NestedDissection<Graph, SubNLG, GenericGraph2D> nd(fg, ordering, cuts);
LONGS_EQUAL(2, nd.root()->size());
LONGS_EQUAL(3, nd.root()->frontal().size());
LONGS_EQUAL(0, nd.root()->separator().size());
LONGS_EQUAL(2, nd.root()->children().size()); // 2 leaf submaps
LONGS_EQUAL(0, nd.root()->weeklinks().size());
// the 1st submap
LONGS_EQUAL(1, nd.root()->children()[0]->frontal().size()); // x0
LONGS_EQUAL(4, nd.root()->children()[0]->size());
LONGS_EQUAL(2, nd.root()->children()[0]->children().size());
// the 1st submap
LONGS_EQUAL(1, nd.root()->children()[0]->frontal().size()); // x0
LONGS_EQUAL(4, nd.root()->children()[0]->size());
LONGS_EQUAL(2, nd.root()->children()[0]->children().size());
// the 1-1st submap
LONGS_EQUAL(1, nd.root()->children()[0]->children()[0]->frontal().size()); // l1
LONGS_EQUAL(2, nd.root()->children()[0]->children()[0]->size());
// the 1-1st submap
LONGS_EQUAL(1, nd.root()->children()[0]->children()[0]->frontal().size()); // l1
LONGS_EQUAL(2, nd.root()->children()[0]->children()[0]->size());
// the 1-2nd submap
LONGS_EQUAL(1, nd.root()->children()[0]->children()[1]->frontal().size()); // l4
LONGS_EQUAL(2, nd.root()->children()[0]->children()[1]->size());
// the 1-2nd submap
LONGS_EQUAL(1, nd.root()->children()[0]->children()[1]->frontal().size()); // l4
LONGS_EQUAL(2, nd.root()->children()[0]->children()[1]->size());
// the 2nd submap
LONGS_EQUAL(1, nd.root()->children()[1]->frontal().size()); // x2
LONGS_EQUAL(3, nd.root()->children()[1]->size());
LONGS_EQUAL(2, nd.root()->children()[1]->children().size());
// the 2nd submap
LONGS_EQUAL(1, nd.root()->children()[1]->frontal().size()); // x2
LONGS_EQUAL(3, nd.root()->children()[1]->size());
LONGS_EQUAL(2, nd.root()->children()[1]->children().size());
// the 2-1st submap
LONGS_EQUAL(1, nd.root()->children()[1]->children()[0]->frontal().size()); // l3
LONGS_EQUAL(2, nd.root()->children()[1]->children()[0]->size());
// the 2-1st submap
LONGS_EQUAL(1, nd.root()->children()[1]->children()[0]->frontal().size()); // l3
LONGS_EQUAL(2, nd.root()->children()[1]->children()[0]->size());
// the 2-2nd submap
LONGS_EQUAL(1, nd.root()->children()[1]->children()[1]->frontal().size()); // l6
LONGS_EQUAL(2, nd.root()->children()[1]->children()[1]->size());
// the 2-2nd submap
LONGS_EQUAL(1, nd.root()->children()[1]->children()[1]->frontal().size()); // l6
LONGS_EQUAL(2, nd.root()->children()[1]->children()[1]->size());
}
@ -272,65 +272,65 @@ TEST ( NestedDissection, manual_cuts )
// / | / \ | \
// x0 x1 x2 x3
TEST( NestedDissection, Graph3D) {
using namespace gtsam::submapVisualSLAM;
typedef TSAM3D::SubNLG SubNLG;
typedef partition::PartitionTable PartitionTable;
vector<GeneralCamera> cameras;
cameras.push_back(GeneralCamera(Pose3(Rot3(), Point3(-2., 0., 0.))));
cameras.push_back(GeneralCamera(Pose3(Rot3(), Point3(-1., 0., 0.))));
cameras.push_back(GeneralCamera(Pose3(Rot3(), Point3( 1., 0., 0.))));
cameras.push_back(GeneralCamera(Pose3(Rot3(), Point3( 2., 0., 0.))));
using namespace gtsam::submapVisualSLAM;
typedef TSAM3D::SubNLG SubNLG;
typedef partition::PartitionTable PartitionTable;
vector<GeneralCamera> cameras;
cameras.push_back(GeneralCamera(Pose3(Rot3(), Point3(-2., 0., 0.))));
cameras.push_back(GeneralCamera(Pose3(Rot3(), Point3(-1., 0., 0.))));
cameras.push_back(GeneralCamera(Pose3(Rot3(), Point3( 1., 0., 0.))));
cameras.push_back(GeneralCamera(Pose3(Rot3(), Point3( 2., 0., 0.))));
vector<Point3> points;
for (int cube_index = 0; cube_index <= 3; cube_index++) {
Point3 center((cube_index-1) * 3, 0.5, 10.);
points.push_back(center + Point3(-0.5, -0.5, -0.5));
points.push_back(center + Point3(-0.5, 0.5, -0.5));
points.push_back(center + Point3( 0.5, 0.5, -0.5));
points.push_back(center + Point3( 0.5, -0.5, -0.5));
points.push_back(center + Point3(-0.5, -0.5, 0.5));
points.push_back(center + Point3(-0.5, 0.5, 0.5));
points.push_back(center + Point3( 0.5, 0.5, 0.5));
points.push_back(center + Point3( 0.5, 0.5, 0.5));
}
vector<Point3> points;
for (int cube_index = 0; cube_index <= 3; cube_index++) {
Point3 center((cube_index-1) * 3, 0.5, 10.);
points.push_back(center + Point3(-0.5, -0.5, -0.5));
points.push_back(center + Point3(-0.5, 0.5, -0.5));
points.push_back(center + Point3( 0.5, 0.5, -0.5));
points.push_back(center + Point3( 0.5, -0.5, -0.5));
points.push_back(center + Point3(-0.5, -0.5, 0.5));
points.push_back(center + Point3(-0.5, 0.5, 0.5));
points.push_back(center + Point3( 0.5, 0.5, 0.5));
points.push_back(center + Point3( 0.5, 0.5, 0.5));
}
Graph graph;
SharedDiagonal measurementNoise(gtsam::Vector_(2, 1., 1.));
SharedDiagonal measurementZeroNoise(gtsam::Vector_(2, 0., 0.));
for (int j=1; j<=8; j++)
graph.addMeasurement(0, j, cameras[0].project(points[j-1]).expmap(measurementZeroNoise->sample()), measurementNoise);
for (int j=1; j<=16; j++)
graph.addMeasurement(1, j, cameras[1].project(points[j-1]).expmap(measurementZeroNoise->sample()), measurementNoise);
for (int j=9; j<=24; j++)
graph.addMeasurement(2, j, cameras[2].project(points[j-1]).expmap(measurementZeroNoise->sample()), measurementNoise);
for (int j=17; j<=24; j++)
graph.addMeasurement(3, j, cameras[3].project(points[j-1]).expmap(measurementZeroNoise->sample()), measurementNoise);
Graph graph;
SharedDiagonal measurementNoise(gtsam::Vector_(2, 1., 1.));
SharedDiagonal measurementZeroNoise(gtsam::Vector_(2, 0., 0.));
for (int j=1; j<=8; j++)
graph.addMeasurement(0, j, cameras[0].project(points[j-1]).expmap(measurementZeroNoise->sample()), measurementNoise);
for (int j=1; j<=16; j++)
graph.addMeasurement(1, j, cameras[1].project(points[j-1]).expmap(measurementZeroNoise->sample()), measurementNoise);
for (int j=9; j<=24; j++)
graph.addMeasurement(2, j, cameras[2].project(points[j-1]).expmap(measurementZeroNoise->sample()), measurementNoise);
for (int j=17; j<=24; j++)
graph.addMeasurement(3, j, cameras[3].project(points[j-1]).expmap(measurementZeroNoise->sample()), measurementNoise);
// make an easy ordering
Ordering ordering; ordering += x0, x1, x2, x3;
for (int j=1; j<=24; j++)
ordering += Symbol('l', j);
// make an easy ordering
Ordering ordering; ordering += x0, x1, x2, x3;
for (int j=1; j<=24; j++)
ordering += Symbol('l', j);
// nested dissection
const int numNodeStopPartition = 10;
const int minNodesPerMap = 5;
NestedDissection<Graph, SubNLG, GenericGraph3D> nd(graph, ordering, numNodeStopPartition, minNodesPerMap);
// nested dissection
const int numNodeStopPartition = 10;
const int minNodesPerMap = 5;
NestedDissection<Graph, SubNLG, GenericGraph3D> nd(graph, ordering, numNodeStopPartition, minNodesPerMap);
LONGS_EQUAL(0, nd.root()->size());
LONGS_EQUAL(8, nd.root()->frontal().size()); // l9-l16
LONGS_EQUAL(0, nd.root()->separator().size());
LONGS_EQUAL(2, nd.root()->children().size()); // 2 leaf submaps
LONGS_EQUAL(0, nd.root()->weeklinks().size());
LONGS_EQUAL(0, nd.root()->size());
LONGS_EQUAL(8, nd.root()->frontal().size()); // l9-l16
LONGS_EQUAL(0, nd.root()->separator().size());
LONGS_EQUAL(2, nd.root()->children().size()); // 2 leaf submaps
LONGS_EQUAL(0, nd.root()->weeklinks().size());
// the 1st submap
LONGS_EQUAL(10, nd.root()->children()[0]->frontal().size()); // x0, x1, l1-l8
LONGS_EQUAL(24, nd.root()->children()[0]->size()); // 8 + 16
LONGS_EQUAL(0, nd.root()->children()[0]->children().size());
// the 1st submap
LONGS_EQUAL(10, nd.root()->children()[0]->frontal().size()); // x0, x1, l1-l8
LONGS_EQUAL(24, nd.root()->children()[0]->size()); // 8 + 16
LONGS_EQUAL(0, nd.root()->children()[0]->children().size());
// the 2nd submap
LONGS_EQUAL(10, nd.root()->children()[1]->frontal().size()); // x2, x3, l1-l8
LONGS_EQUAL(24, nd.root()->children()[1]->size()); // 16 + 8
LONGS_EQUAL(0, nd.root()->children()[1]->children().size());
// the 2nd submap
LONGS_EQUAL(10, nd.root()->children()[1]->frontal().size()); // x2, x3, l1-l8
LONGS_EQUAL(24, nd.root()->children()[1]->size()); // 16 + 8
LONGS_EQUAL(0, nd.root()->children()[1]->children().size());
}

100
gtsam_unstable/slam/BetweenFactorEM.h
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@ -295,87 +295,87 @@ namespace gtsam {
/* ************************************************************************* */
SharedGaussian get_model_inlier() const {
return model_inlier_;
return model_inlier_;
}
/* ************************************************************************* */
SharedGaussian get_model_outlier() const {
return model_outlier_;
return model_outlier_;
}
/* ************************************************************************* */
Matrix get_model_inlier_cov() const {
return (model_inlier_->R().transpose()*model_inlier_->R()).inverse();
return (model_inlier_->R().transpose()*model_inlier_->R()).inverse();
}
/* ************************************************************************* */
Matrix get_model_outlier_cov() const {
return (model_outlier_->R().transpose()*model_outlier_->R()).inverse();
return (model_outlier_->R().transpose()*model_outlier_->R()).inverse();
}
/* ************************************************************************* */
void updateNoiseModels(const gtsam::Values& values, const gtsam::NonlinearFactorGraph& graph){
/* Update model_inlier_ and model_outlier_ to account for uncertainty in robot trajectories
* (note these are given in the E step, where indicator probabilities are calculated).
*
* Principle: R += [H1 H2] * joint_cov12 * [H1 H2]', where H1, H2 are Jacobians of the
* unwhitened error w.r.t. states, and R is the measurement covariance (inlier or outlier modes).
*
* TODO: improve efficiency (info form)
*/
/* Update model_inlier_ and model_outlier_ to account for uncertainty in robot trajectories
* (note these are given in the E step, where indicator probabilities are calculated).
*
* Principle: R += [H1 H2] * joint_cov12 * [H1 H2]', where H1, H2 are Jacobians of the
* unwhitened error w.r.t. states, and R is the measurement covariance (inlier or outlier modes).
*
* TODO: improve efficiency (info form)
*/
// get joint covariance of the involved states
std::vector<gtsam::Key> Keys;
Keys.push_back(key1_);
Keys.push_back(key2_);
Marginals marginals( graph, values, Marginals::QR );
JointMarginal joint_marginal12 = marginals.jointMarginalCovariance(Keys);
Matrix cov1 = joint_marginal12(key1_, key1_);
Matrix cov2 = joint_marginal12(key2_, key2_);
Matrix cov12 = joint_marginal12(key1_, key2_);
// get joint covariance of the involved states
std::vector<gtsam::Key> Keys;
Keys.push_back(key1_);
Keys.push_back(key2_);
Marginals marginals( graph, values, Marginals::QR );
JointMarginal joint_marginal12 = marginals.jointMarginalCovariance(Keys);
Matrix cov1 = joint_marginal12(key1_, key1_);
Matrix cov2 = joint_marginal12(key2_, key2_);
Matrix cov12 = joint_marginal12(key1_, key2_);
updateNoiseModels_givenCovs(values, cov1, cov2, cov12);
updateNoiseModels_givenCovs(values, cov1, cov2, cov12);
}
/* ************************************************************************* */
void updateNoiseModels_givenCovs(const gtsam::Values& values, const Matrix& cov1, const Matrix& cov2, const Matrix& cov12){
/* Update model_inlier_ and model_outlier_ to account for uncertainty in robot trajectories
* (note these are given in the E step, where indicator probabilities are calculated).
*
* Principle: R += [H1 H2] * joint_cov12 * [H1 H2]', where H1, H2 are Jacobians of the
* unwhitened error w.r.t. states, and R is the measurement covariance (inlier or outlier modes).
*
* TODO: improve efficiency (info form)
*/
/* Update model_inlier_ and model_outlier_ to account for uncertainty in robot trajectories
* (note these are given in the E step, where indicator probabilities are calculated).
*
* Principle: R += [H1 H2] * joint_cov12 * [H1 H2]', where H1, H2 are Jacobians of the
* unwhitened error w.r.t. states, and R is the measurement covariance (inlier or outlier modes).
*
* TODO: improve efficiency (info form)
*/
const T& p1 = values.at<T>(key1_);
const T& p2 = values.at<T>(key2_);
const T& p1 = values.at<T>(key1_);
const T& p2 = values.at<T>(key2_);
Matrix H1, H2;
T hx = p1.between(p2, H1, H2); // h(x)
Matrix H1, H2;
T hx = p1.between(p2, H1, H2); // h(x)
Matrix H;
H.resize(H1.rows(), H1.rows()+H2.rows());
H << H1, H2; // H = [H1 H2]
Matrix H;
H.resize(H1.rows(), H1.rows()+H2.rows());
H << H1, H2; // H = [H1 H2]
Matrix joint_cov;
joint_cov.resize(cov1.rows()+cov2.rows(), cov1.cols()+cov2.cols());
joint_cov << cov1, cov12,
cov12.transpose(), cov2;
Matrix joint_cov;
joint_cov.resize(cov1.rows()+cov2.rows(), cov1.cols()+cov2.cols());
joint_cov << cov1, cov12,
cov12.transpose(), cov2;
Matrix cov_state = H*joint_cov*H.transpose();
Matrix cov_state = H*joint_cov*H.transpose();
// model_inlier_->print("before:");
// model_inlier_->print("before:");
// update inlier and outlier noise models
Matrix covRinlier = (model_inlier_->R().transpose()*model_inlier_->R()).inverse();
model_inlier_ = gtsam::noiseModel::Gaussian::Covariance(covRinlier + cov_state);
// update inlier and outlier noise models
Matrix covRinlier = (model_inlier_->R().transpose()*model_inlier_->R()).inverse();
model_inlier_ = gtsam::noiseModel::Gaussian::Covariance(covRinlier + cov_state);
Matrix covRoutlier = (model_outlier_->R().transpose()*model_outlier_->R()).inverse();
model_outlier_ = gtsam::noiseModel::Gaussian::Covariance(covRoutlier + cov_state);
Matrix covRoutlier = (model_outlier_->R().transpose()*model_outlier_->R()).inverse();
model_outlier_ = gtsam::noiseModel::Gaussian::Covariance(covRoutlier + cov_state);
// model_inlier_->print("after:");
// std::cout<<"covRinlier + cov_state: "<<covRinlier + cov_state<<std::endl;
// model_inlier_->print("after:");
// std::cout<<"covRinlier + cov_state: "<<covRinlier + cov_state<<std::endl;
}
/* ************************************************************************* */

105
gtsam_unstable/slam/BiasedGPSFactor.h Normal file
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@ -0,0 +1,105 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file BiasedGPSFactor.h
* @author Luca Carlone
**/
#pragma once
#include <ostream>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/nonlinear/NonlinearFactor.h>
namespace gtsam {
/**
* A class to model GPS measurements, including a bias term which models
* common-mode errors and that can be partially corrected if other sensors are used
* @addtogroup SLAM
*/
class BiasedGPSFactor: public NoiseModelFactor2<Pose3, Point3> {
private:
typedef BiasedGPSFactor This;
typedef NoiseModelFactor2<Pose3, Point3> Base;
Point3 measured_; /** The measurement */
public:
// shorthand for a smart pointer to a factor
typedef boost::shared_ptr<BiasedGPSFactor> shared_ptr;
/** default constructor - only use for serialization */
BiasedGPSFactor() {}
/** Constructor */
BiasedGPSFactor(Key posekey, Key biaskey, const Point3 measured,
const SharedNoiseModel& model) :
Base(model, posekey, biaskey), measured_(measured) {
}
virtual ~BiasedGPSFactor() {}
/** implement functions needed for Testable */
/** print */
virtual void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
std::cout << s << "BiasedGPSFactor("
<< keyFormatter(this->key1()) << ","
<< keyFormatter(this->key2()) << ")\n";
measured_.print(" measured: ");
this->noiseModel_->print(" noise model: ");
}
/** equals */
virtual bool equals(const NonlinearFactor& expected, double tol=1e-9) const {
const This *e = dynamic_cast<const This*> (&expected);
return e != NULL && Base::equals(*e, tol) && this->measured_.equals(e->measured_, tol);
}
/** implement functions needed to derive from Factor */
/** vector of errors */
Vector evaluateError(const Pose3& pose, const Point3& bias,
boost::optional<Matrix&> H1 = boost::none, boost::optional<Matrix&> H2 =
boost::none) const {
if (H1 || H2){
H1->resize(3,6); // jacobian wrt pose
(*H1) << Matrix3::Zero(), pose.rotation().matrix();
H2->resize(3,3); // jacobian wrt bias
(*H2) << Matrix3::Identity();
}
return pose.translation().vector() + bias.vector() - measured_.vector();
}
/** return the measured */
const Point3 measured() const {
return measured_;
}
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & boost::serialization::make_nvp("NoiseModelFactor2",
boost::serialization::base_object<Base>(*this));
ar & BOOST_SERIALIZATION_NVP(measured_);
}
}; // \class BiasedGPSFactor
} /// namespace gtsam

135
gtsam_unstable/slam/GaussMarkov1stOrderFactor.h Normal file
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@ -0,0 +1,135 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file GaussMarkov1stOrderFactor.h
* @author Vadim Indelman, Stephen Williams, Luca Carlone
* @date Jan 17, 2012
**/
#pragma once
#include <ostream>
#include <gtsam/base/Testable.h>
#include <gtsam/base/Lie.h>
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/linear/GaussianFactor.h>
#include <gtsam/linear/NoiseModel.h>
namespace gtsam {
/*
* - The 1st order GaussMarkov factor relates two keys of the same type. This relation is given via
* key_2 = exp(-1/tau*delta_t) * key1 + w_d
* where tau is the time constant and delta_t is the time difference between the two keys.
* w_d is the equivalent discrete noise, whose covariance is calculated from the continuous noise model and delta_t.
* - w_d is approximated as a Gaussian noise.
* - In the multi-dimensional case, tau is a vector, and the above equation is applied on each element
* in the state (represented by keys), using the appropriate time constant in the vector tau.
*/
/*
* A class for a measurement predicted by "GaussMarkov1stOrderFactor(config[key1],config[key2])"
* KEY1::Value is the Lie Group type
* T is the measurement type, by default the same
*/
template<class VALUE>
class GaussMarkov1stOrderFactor: public NoiseModelFactor2<VALUE, VALUE> {
private:
typedef GaussMarkov1stOrderFactor<VALUE> This;
typedef NoiseModelFactor2<VALUE, VALUE> Base;
double dt_;
Vector tau_;
public:
// shorthand for a smart pointer to a factor
typedef typename boost::shared_ptr<GaussMarkov1stOrderFactor> shared_ptr;
/** default constructor - only use for serialization */
GaussMarkov1stOrderFactor() {}
/** Constructor */
GaussMarkov1stOrderFactor(const Key& key1, const Key& key2, double delta_t, Vector tau,
const SharedGaussian& model) :
Base(calcDiscreteNoiseModel(model, delta_t), key1, key2), dt_(delta_t), tau_(tau) {
}
virtual ~GaussMarkov1stOrderFactor() {}
/** implement functions needed for Testable */
/** print */
virtual void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
std::cout << s << "GaussMarkov1stOrderFactor("
<< keyFormatter(this->key1()) << ","
<< keyFormatter(this->key2()) << ")\n";
this->noiseModel_->print(" noise model");
}
/** equals */
virtual bool equals(const NonlinearFactor& expected, double tol=1e-9) const {
const This *e = dynamic_cast<const This*> (&expected);
return e != NULL && Base::equals(*e, tol);
}
/** implement functions needed to derive from Factor */
/** vector of errors */
Vector evaluateError(const VALUE& p1, const VALUE& p2,
boost::optional<Matrix&> H1 = boost::none,
boost::optional<Matrix&> H2 = boost::none) const {
Vector v1( VALUE::Logmap(p1) );
Vector v2( VALUE::Logmap(p2) );
Vector alpha(tau_.size());
Vector alpha_v1(tau_.size());
for(int i=0; i<tau_.size(); i++){
alpha(i) = exp(- 1/tau_(i)*dt_ );
alpha_v1(i) = alpha(i) * v1(i);
}
Vector hx(v2 - alpha_v1);
if(H1) *H1 = - diag(alpha);
if(H2) *H2 = eye(v2.size());
return hx;
}
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
ar & BOOST_SERIALIZATION_NVP(dt_);
ar & BOOST_SERIALIZATION_NVP(tau_);
}
SharedGaussian calcDiscreteNoiseModel(const SharedGaussian& model, double delta_t){
/* Q_d (approx)= Q * delta_t */
/* In practice, square root of the information matrix is represented, so that:
* R_d (approx)= R / sqrt(delta_t)
* */
noiseModel::Gaussian::shared_ptr gaussian_model = boost::dynamic_pointer_cast<noiseModel::Gaussian>(model);
SharedGaussian model_d(noiseModel::Gaussian::SqrtInformation(gaussian_model->R()/sqrt(delta_t)));
return model_d;
}
}; // \class GaussMarkov1stOrderFactor
} /// namespace gtsam

54
gtsam_unstable/slam/tests/testBetweenFactorEM.cpp
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@ -255,43 +255,43 @@ TEST( BetweenFactorEM, CaseStudy)
///* ************************************************************************** */
TEST (BetweenFactorEM, updateNoiseModel ) {
gtsam::Key key1(1);
gtsam::Key key2(2);
gtsam::Key key1(1);
gtsam::Key key2(2);
gtsam::Pose2 p1(10.0, 15.0, 0.1);
gtsam::Pose2 p2(15.0, 15.0, 0.3);
gtsam::Pose2 noise(0.5, 0.4, 0.01);
gtsam::Pose2 rel_pose_ideal = p1.between(p2);
gtsam::Pose2 rel_pose_msr = rel_pose_ideal.compose(noise);
gtsam::Pose2 p1(10.0, 15.0, 0.1);
gtsam::Pose2 p2(15.0, 15.0, 0.3);
gtsam::Pose2 noise(0.5, 0.4, 0.01);
gtsam::Pose2 rel_pose_ideal = p1.between(p2);
gtsam::Pose2 rel_pose_msr = rel_pose_ideal.compose(noise);
SharedGaussian model_inlier(noiseModel::Diagonal::Sigmas( (gtsam::Vector(3) << 1.5, 2.5, 4.05)));
SharedGaussian model_outlier(noiseModel::Diagonal::Sigmas( (gtsam::Vector(3) << 50.0, 50.0, 10.0)));
SharedGaussian model_inlier(noiseModel::Diagonal::Sigmas( (gtsam::Vector(3) << 1.5, 2.5, 4.05)));
SharedGaussian model_outlier(noiseModel::Diagonal::Sigmas( (gtsam::Vector(3) << 50.0, 50.0, 10.0)));
gtsam::Values values;
values.insert(key1, p1);
values.insert(key2, p2);
gtsam::Values values;
values.insert(key1, p1);
values.insert(key2, p2);
double prior_outlier = 0.0;
double prior_inlier = 1.0;
double prior_outlier = 0.0;
double prior_inlier = 1.0;
BetweenFactorEM<gtsam::Pose2> f(key1, key2, rel_pose_msr, model_inlier, model_outlier,
prior_inlier, prior_outlier);
BetweenFactorEM<gtsam::Pose2> f(key1, key2, rel_pose_msr, model_inlier, model_outlier,
prior_inlier, prior_outlier);
SharedGaussian model = SharedGaussian(noiseModel::Isotropic::Sigma(3, 1e2));
SharedGaussian model = SharedGaussian(noiseModel::Isotropic::Sigma(3, 1e2));
NonlinearFactorGraph graph;
graph.push_back(gtsam::PriorFactor<Pose2>(key1, p1, model));
graph.push_back(gtsam::PriorFactor<Pose2>(key2, p2, model));
NonlinearFactorGraph graph;
graph.push_back(gtsam::PriorFactor<Pose2>(key1, p1, model));
graph.push_back(gtsam::PriorFactor<Pose2>(key2, p2, model));
f.updateNoiseModels(values, graph);
f.updateNoiseModels(values, graph);
SharedGaussian model_inlier_new = f.get_model_inlier();
SharedGaussian model_outlier_new = f.get_model_outlier();
SharedGaussian model_inlier_new = f.get_model_inlier();
SharedGaussian model_outlier_new = f.get_model_outlier();
model_inlier->print("model_inlier:");
model_outlier->print("model_outlier:");
model_inlier_new->print("model_inlier_new:");
model_outlier_new->print("model_outlier_new:");
model_inlier->print("model_inlier:");
model_outlier->print("model_outlier:");
model_inlier_new->print("model_inlier_new:");
model_outlier_new->print("model_outlier_new:");
}

85
gtsam_unstable/slam/tests/testBiasedGPSFactor.cpp Normal file
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@ -0,0 +1,85 @@
/**
* @file testBiasedGPSFactor.cpp
* @brief
* @author Luca Carlone
* @date July 30, 2014
*/
#include <gtsam/base/numericalDerivative.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam_unstable/slam/BiasedGPSFactor.h>
#include <CppUnitLite/TestHarness.h>
using namespace gtsam;
using namespace gtsam::symbol_shorthand;
using namespace gtsam::noiseModel;
// Convenience for named keys
using symbol_shorthand::X;
using symbol_shorthand::B;
TEST(BiasedGPSFactor, errorNoiseless) {
Rot3 R = Rot3::rodriguez(0.1, 0.2, 0.3);
Point3 t(1.0, 0.5, 0.2);
Pose3 pose(R,t);
Point3 bias(0.0,0.0,0.0);
Point3 noise(0.0,0.0,0.0);
Point3 measured = t + noise;
BiasedGPSFactor factor(X(1), B(1), measured, Isotropic::Sigma(3, 0.05));
Vector expectedError = (Vector(3) << 0.0, 0.0, 0.0 );
Vector actualError = factor.evaluateError(pose,bias);
EXPECT(assert_equal(expectedError,actualError, 1E-5));
}
TEST(BiasedGPSFactor, errorNoisy) {
Rot3 R = Rot3::rodriguez(0.1, 0.2, 0.3);
Point3 t(1.0, 0.5, 0.2);
Pose3 pose(R,t);
Point3 bias(0.0,0.0,0.0);
Point3 noise(1.0,2.0,3.0);
Point3 measured = t - noise;
BiasedGPSFactor factor(X(1), B(1), measured, Isotropic::Sigma(3, 0.05));
Vector expectedError = (Vector(3) << 1.0, 2.0, 3.0 );
Vector actualError = factor.evaluateError(pose,bias);
EXPECT(assert_equal(expectedError,actualError, 1E-5));
}
TEST(BiasedGPSFactor, jacobian) {
Rot3 R = Rot3::rodriguez(0.1, 0.2, 0.3);
Point3 t(1.0, 0.5, 0.2);
Pose3 pose(R,t);
Point3 bias(0.0,0.0,0.0);
Point3 noise(0.0,0.0,0.0);
Point3 measured = t + noise;
BiasedGPSFactor factor(X(1), B(1), measured, Isotropic::Sigma(3, 0.05));
Matrix actualH1, actualH2;
factor.evaluateError(pose,bias, actualH1, actualH2);
Matrix numericalH1 = numericalDerivative21(
boost::function<Vector(const Pose3&, const Point3&)>(boost::bind(
&BiasedGPSFactor::evaluateError, factor, _1, _2, boost::none,
boost::none)), pose, bias, 1e-5);
EXPECT(assert_equal(numericalH1,actualH1, 1E-5));
Matrix numericalH2 = numericalDerivative22(
boost::function<Vector(const Pose3&, const Point3&)>(boost::bind(
&BiasedGPSFactor::evaluateError, factor, _1, _2, boost::none,
boost::none)), pose, bias, 1e-5);
EXPECT(assert_equal(numericalH2,actualH2, 1E-5));
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

124
gtsam_unstable/slam/tests/testGaussMarkov1stOrderFactor.cpp Normal file
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@ -0,0 +1,124 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file testGaussMarkov1stOrderFactor.cpp
* @brief Unit tests for the GaussMarkov1stOrder factor
* @author Vadim Indelman
* @date Jan 17, 2012
*/
#include <gtsam_unstable/slam/GaussMarkov1stOrderFactor.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/inference/Key.h>
#include <gtsam/base/numericalDerivative.h>
#include <gtsam/base/LieVector.h>
#include <CppUnitLite/TestHarness.h>
using namespace std;
using namespace gtsam;
//! Factors
typedef GaussMarkov1stOrderFactor<LieVector> GaussMarkovFactor;
/* ************************************************************************* */
LieVector predictionError(const LieVector& v1, const LieVector& v2, const GaussMarkovFactor factor) {
return factor.evaluateError(v1, v2);
}
/* ************************************************************************* */
TEST( GaussMarkovFactor, equals )
{
// Create two identical factors and make sure they're equal
Key x1(1);
Key x2(2);
double delta_t = 0.10;
Vector tau = (Vector(3) << 100.0, 150.0, 10.0);
SharedGaussian model = noiseModel::Isotropic::Sigma(3, 1.0);
GaussMarkovFactor factor1(x1, x2, delta_t, tau, model);
GaussMarkovFactor factor2(x1, x2, delta_t, tau, model);
CHECK(assert_equal(factor1, factor2));
}
/* ************************************************************************* */
TEST( GaussMarkovFactor, error )
{
Values linPoint;
Key x1(1);
Key x2(2);
double delta_t = 0.10;
Vector tau = (Vector(3) << 100.0, 150.0, 10.0);
SharedGaussian model = noiseModel::Isotropic::Sigma(3, 1.0);
LieVector v1 = LieVector((Vector(3) << 10.0, 12.0, 13.0));
LieVector v2 = LieVector((Vector(3) << 10.0, 15.0, 14.0));
// Create two nodes
linPoint.insert(x1, v1);
linPoint.insert(x2, v2);
GaussMarkovFactor factor(x1, x2, delta_t, tau, model);
Vector Err1( factor.evaluateError(v1, v2) );
// Manually calculate the error
Vector alpha(tau.size());
Vector alpha_v1(tau.size());
for(int i=0; i<tau.size(); i++){
alpha(i) = exp(- 1/tau(i)*delta_t );
alpha_v1(i) = alpha(i) * v1(i);
}
Vector Err2( v2 - alpha_v1 );
CHECK(assert_equal(Err1, Err2, 1e-9));
}
/* ************************************************************************* */
TEST (GaussMarkovFactor, jacobian ) {
Values linPoint;
Key x1(1);
Key x2(2);
double delta_t = 0.10;
Vector tau = (Vector(3) << 100.0, 150.0, 10.0);
SharedGaussian model = noiseModel::Isotropic::Sigma(3, 1.0);
GaussMarkovFactor factor(x1, x2, delta_t, tau, model);
// Update the linearization point
LieVector v1_upd = LieVector((Vector(3) << 0.5, -0.7, 0.3));
LieVector v2_upd = LieVector((Vector(3) << -0.7, 0.4, 0.9));
// Calculate the Jacobian matrix using the factor
Matrix computed_H1, computed_H2;
factor.evaluateError(v1_upd, v2_upd, computed_H1, computed_H2);
// Calculate the Jacobian matrices H1 and H2 using the numerical derivative function
Matrix numerical_H1, numerical_H2;
numerical_H1 = numericalDerivative21<Vector3, Vector3, Vector3>(
boost::bind(&predictionError, _1, _2, factor), v1_upd, v2_upd);
numerical_H2 = numericalDerivative22<Vector3, Vector3, Vector3>(
boost::bind(&predictionError, _1, _2, factor), v1_upd, v2_upd);
// Verify they are equal for this choice of state
CHECK( assert_equal(numerical_H1, computed_H1, 1e-9));
CHECK( assert_equal(numerical_H2, computed_H2, 1e-9));
}
/* ************************************************************************* */
int main()
{
TestResult tr; return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

26
timing/timePinholeCamera.cpp
View File

@ -26,7 +26,7 @@ using namespace gtsam;
int main()
{
int n = 1000000;
int n = 1e6;
const Pose3 pose1((Matrix)(Matrix(3,3) <<
1., 0., 0.,
@ -35,8 +35,6 @@ int main()
),
Point3(0,0,0.5));
// static Cal3_S2 K(500, 100, 0.1, 320, 240);
// static Cal3DS2 K(500, 100, 0.1, 320, 240, 1e-3, 2.0*1e-3, 3.0*1e-3, 4.0*1e-3);
static Cal3Bundler K(500, 1e-3, 2.0*1e-3);
const PinholeCamera<Cal3Bundler> camera(pose1,K);
const Point3 point1(-0.08,-0.08, 0.0);
@ -63,8 +61,18 @@ int main()
camera.project(point1);
long timeLog2 = clock();
double seconds = (double)(timeLog2-timeLog)/CLOCKS_PER_SEC;
cout << ((double)n/seconds) << " calls/second" << endl;
cout << ((double)seconds*1000000/n) << " musecs/call" << endl;
cout << ((double)seconds*1e9/n) << " nanosecs/call" << endl;
}
// Oct 12 2014, Macbook Air
{
long timeLog = clock();
Point2 measurement(0,0);
for(int i = 0; i < n; i++)
measurement.localCoordinates(camera.project(point1));
long timeLog2 = clock();
double seconds = (double)(timeLog2-timeLog)/CLOCKS_PER_SEC;
cout << ((double)seconds*1e9/n) << " nanosecs/call" << endl;
}
// Oct 12 2013, iMac 3.06GHz Core i3
@ -84,8 +92,7 @@ int main()
camera.project(point1, Dpose, Dpoint);
long timeLog2 = clock();
double seconds = (double)(timeLog2-timeLog)/CLOCKS_PER_SEC;
cout << ((double)n/seconds) << " calls/second" << endl;
cout << ((double)seconds*1000000/n) << " musecs/call" << endl;
cout << ((double)seconds*1e9/n) << " nanosecs/call" << endl;
}
// Oct 12 2013, iMac 3.06GHz Core i3
@ -97,7 +104,7 @@ int main()
// Cal3Bundler fix: 2.0946 musecs/call
// June 24 2014, Macbook Pro 2.3GHz Core i7
// GTSAM 3.1: 0.2294 musecs/call
// After project fix: 0.2093 musecs/call
// After project fix: 0.2093 nanosecs/call
{
Matrix Dpose, Dpoint, Dcal;
long timeLog = clock();
@ -105,8 +112,7 @@ int main()
camera.project(point1, Dpose, Dpoint, Dcal);
long timeLog2 = clock();
double seconds = (double)(timeLog2-timeLog)/CLOCKS_PER_SEC;
cout << ((double)n/seconds) << " calls/second" << endl;
cout << ((double)seconds*1000000/n) << " musecs/call" << endl;
cout << ((double)seconds*1e9/n) << " nanosecs/call" << endl;
}
return 0;