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Merge branch 'develop' into feature/python-plotting

release/4.3a0
Varun Agrawal 2020-04-08 19:36:47 -04:00
parent a59682e608 5c9c4bed93
commit fec98b5cd4
83 changed files with 558 additions and 227 deletions
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35
.travis.sh
View File

@ -1,5 +1,37 @@
#!/bin/bash
# install TBB with _debug.so files
function install_tbb()
{
TBB_BASEURL=https://github.com/oneapi-src/oneTBB/releases/download
TBB_VERSION=4.4.2
TBB_DIR=tbb44_20151115oss
TBB_SAVEPATH="/tmp/tbb.tgz"
if [ "$TRAVIS_OS_NAME" == "linux" ]; then
OS_SHORT="lin"
TBB_LIB_DIR="intel64/gcc4.4"
SUDO="sudo"
elif [ "$TRAVIS_OS_NAME" == "osx" ]; then
OS_SHORT="lin"
TBB_LIB_DIR=""
SUDO=""
fi
wget "${TBB_BASEURL}/${TBB_VERSION}/${TBB_DIR}_${OS_SHORT}.tgz" -O $TBB_SAVEPATH
tar -C /tmp -xf $TBB_SAVEPATH
TBBROOT=/tmp/$TBB_DIR
# Copy the needed files to the correct places.
# This works correctly for travis builds, instead of setting path variables.
# This is what Homebrew does to install TBB on Macs
$SUDO cp -R $TBBROOT/lib/$TBB_LIB_DIR/* /usr/local/lib/
$SUDO cp -R $TBBROOT/include/ /usr/local/include/
}
# common tasks before either build or test
function configure()
{
@ -14,6 +46,8 @@ function configure()
rm -fr $BUILD_DIR || true
mkdir $BUILD_DIR && cd $BUILD_DIR
install_tbb
if [ ! -z "$GCC_VERSION" ]; then
export CC=gcc-$GCC_VERSION
export CXX=g++-$GCC_VERSION
@ -24,6 +58,7 @@ function configure()
-DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:-Debug} \
-DGTSAM_BUILD_TESTS=${GTSAM_BUILD_TESTS:-OFF} \
-DGTSAM_BUILD_UNSTABLE=${GTSAM_BUILD_UNSTABLE:-ON} \
-DGTSAM_WITH_TBB=${GTSAM_WITH_TBB:-OFF} \
-DGTSAM_USE_QUATERNIONS=${GTSAM_USE_QUATERNIONS:-OFF} \
-DGTSAM_BUILD_EXAMPLES_ALWAYS=${GTSAM_BUILD_EXAMPLES_ALWAYS:-ON} \
-DGTSAM_ALLOW_DEPRECATED_SINCE_V4=${GTSAM_ALLOW_DEPRECATED_SINCE_V4:-OFF} \

8
.travis.yml
View File

@ -18,7 +18,7 @@ addons:
- libboost-all-dev
# before_install:
# - if [ "$TRAVIS_OS_NAME" == "osx" ]; then brew update ; fi
# - if [ "$TRAVIS_OS_NAME" == "osx" ]; then brew update; fi
install:
- if [ "$TRAVIS_OS_NAME" == "osx" ]; then HOMEBREW_NO_AUTO_UPDATE=1 brew install ccache ; fi
@ -89,6 +89,12 @@ jobs:
compiler: clang
env: CC=clang-9 CXX=clang++-9 CMAKE_BUILD_TYPE=Debug GTSAM_BUILD_UNSTABLE=OFF GTSAM_ALLOW_DEPRECATED_SINCE_V4=ON
script: bash .travis.sh -b
# on Linux, with GTSAM_WITH_TBB on to make sure GTSAM still compiles/tests
- stage: special
os: linux
compiler: gcc
env: CMAKE_BUILD_TYPE=Debug GTSAM_BUILD_UNSTABLE=OFF GTSAM_WITH_TBB=ON
script: bash .travis.sh -b
# -------- STAGE 2: TESTS -----------
# on Mac, GCC
- stage: test

9
CMakeLists.txt
View File

@ -200,6 +200,11 @@ find_package(TBB 4.4 COMPONENTS tbb tbbmalloc)
# Set up variables if we're using TBB
if(TBB_FOUND AND GTSAM_WITH_TBB)
set(GTSAM_USE_TBB 1) # This will go into config.h
if ((${TBB_VERSION_MAJOR} GREATER 2020) OR (${TBB_VERSION_MAJOR} EQUAL 2020))
set(TBB_GREATER_EQUAL_2020 1)
else()
set(TBB_GREATER_EQUAL_2020 0)
endif()
# all definitions and link requisites will go via imported targets:
# tbb & tbbmalloc
list(APPEND GTSAM_ADDITIONAL_LIBRARIES tbb tbbmalloc)
@ -416,6 +421,10 @@ add_subdirectory(CppUnitLite)
# Build wrap
if (GTSAM_BUILD_WRAP)
add_subdirectory(wrap)
# suppress warning of cython line being too long
if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-misleading-indentation")
endif()
endif(GTSAM_BUILD_WRAP)
# Build GTSAM library

5
cython/gtsam/examples/Pose2SLAMExample_g2o.py
View File

@ -53,16 +53,15 @@ graph, initial = gtsam.readG2o(g2oFile, is3D)
assert args.kernel == "none", "Supplied kernel type is not yet implemented"
# Add prior on the pose having index (key) = 0
graphWithPrior = graph
priorModel = gtsam.noiseModel_Diagonal.Variances(vector3(1e-6, 1e-6, 1e-8))
graphWithPrior.add(gtsam.PriorFactorPose2(0, gtsam.Pose2(), priorModel))
graph.add(gtsam.PriorFactorPose2(0, gtsam.Pose2(), priorModel))
params = gtsam.GaussNewtonParams()
params.setVerbosity("Termination")
params.setMaxIterations(maxIterations)
# parameters.setRelativeErrorTol(1e-5)
# Create the optimizer ...
optimizer = gtsam.GaussNewtonOptimizer(graphWithPrior, initial, params)
optimizer = gtsam.GaussNewtonOptimizer(graph, initial, params)
# ... and optimize
result = optimizer.optimize()

9
cython/gtsam/examples/Pose3SLAMExample_g2o.py
View File

@ -43,18 +43,17 @@ priorModel = gtsam.noiseModel_Diagonal.Variances(vector6(1e-6, 1e-6, 1e-6,
1e-4, 1e-4, 1e-4))
print("Adding prior to g2o file ")
graphWithPrior = graph
firstKey = initial.keys().at(0)
graphWithPrior.add(gtsam.PriorFactorPose3(firstKey, gtsam.Pose3(), priorModel))
graph.add(gtsam.PriorFactorPose3(firstKey, gtsam.Pose3(), priorModel))
params = gtsam.GaussNewtonParams()
params.setVerbosity("Termination") # this will show info about stopping conds
optimizer = gtsam.GaussNewtonOptimizer(graphWithPrior, initial, params)
optimizer = gtsam.GaussNewtonOptimizer(graph, initial, params)
result = optimizer.optimize()
print("Optimization complete")
print("initial error = ", graphWithPrior.error(initial))
print("final error = ", graphWithPrior.error(result))
print("initial error = ", graph.error(initial))
print("final error = ", graph.error(result))
if args.output is None:
print("Final Result:\n{}".format(result))

9
examples/Data/pose3Localizationexample.txt Normal file
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@ -0,0 +1,9 @@
VERTEX_SE3:QUAT 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000
VERTEX_SE3:QUAT 1 1.001367 0.015390 0.004948 0.190253 0.283162 -0.392318 0.854230
VERTEX_SE3:QUAT 2 1.993500 0.023275 0.003793 -0.351729 -0.597838 0.584174 0.421446
VERTEX_SE3:QUAT 3 2.004291 1.024305 0.018047 0.331798 -0.200659 0.919323 0.067024
VERTEX_SE3:QUAT 4 0.999908 1.055073 0.020212 -0.035697 -0.462490 0.445933 0.765488
EDGE_SE3:QUAT 0 1 1.001367 0.015390 0.004948 0.190253 0.283162 -0.392318 0.854230 10000.000000 0.000000 0.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 10000.000000 0.000000 10000.000000
EDGE_SE3:QUAT 1 2 0.523923 0.776654 0.326659 0.311512 0.656877 -0.678505 0.105373 10000.000000 0.000000 0.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 10000.000000 0.000000 10000.000000
EDGE_SE3:QUAT 2 3 0.910927 0.055169 -0.411761 0.595795 -0.561677 0.079353 0.568551 10000.000000 0.000000 0.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 10000.000000 0.000000 10000.000000
EDGE_SE3:QUAT 3 4 0.775288 0.228798 -0.596923 -0.592077 0.303380 -0.513226 0.542221 10000.000000 0.000000 0.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 0.000000 10000.000000 0.000000 0.000000 10000.000000 0.000000 10000.000000

5
examples/Pose2SLAMExample_g2o.cpp
View File

@ -63,10 +63,9 @@ int main(const int argc, const char *argv[]) {
}
// Add prior on the pose having index (key) = 0
NonlinearFactorGraph graphWithPrior = *graph;
noiseModel::Diagonal::shared_ptr priorModel = //
noiseModel::Diagonal::Variances(Vector3(1e-6, 1e-6, 1e-8));
graphWithPrior.add(PriorFactor<Pose2>(0, Pose2(), priorModel));
graph->add(PriorFactor<Pose2>(0, Pose2(), priorModel));
std::cout << "Adding prior on pose 0 " << std::endl;
GaussNewtonParams params;
@ -77,7 +76,7 @@ int main(const int argc, const char *argv[]) {
}
std::cout << "Optimizing the factor graph" << std::endl;
GaussNewtonOptimizer optimizer(graphWithPrior, *initial, params);
GaussNewtonOptimizer optimizer(*graph, *initial, params);
Values result = optimizer.optimize();
std::cout << "Optimization complete" << std::endl;

12
examples/Pose2SLAMExample_lago.cpp
View File

@ -42,14 +42,13 @@ int main(const int argc, const char *argv[]) {
boost::tie(graph, initial) = readG2o(g2oFile);
// Add prior on the pose having index (key) = 0
NonlinearFactorGraph graphWithPrior = *graph;
noiseModel::Diagonal::shared_ptr priorModel = //
noiseModel::Diagonal::Variances(Vector3(1e-6, 1e-6, 1e-8));
graphWithPrior.add(PriorFactor<Pose2>(0, Pose2(), priorModel));
graphWithPrior.print();
graph->add(PriorFactor<Pose2>(0, Pose2(), priorModel));
graph->print();
std::cout << "Computing LAGO estimate" << std::endl;
Values estimateLago = lago::initialize(graphWithPrior);
Values estimateLago = lago::initialize(*graph);
std::cout << "done!" << std::endl;
if (argc < 3) {
@ -57,7 +56,10 @@ int main(const int argc, const char *argv[]) {
} else {
const string outputFile = argv[2];
std::cout << "Writing results to file: " << outputFile << std::endl;
writeG2o(*graph, estimateLago, outputFile);
NonlinearFactorGraph::shared_ptr graphNoKernel;
Values::shared_ptr initial2;
boost::tie(graphNoKernel, initial2) = readG2o(g2oFile);
writeG2o(*graphNoKernel, estimateLago, outputFile);
std::cout << "done! " << std::endl;
}

85
examples/Pose3Localization.cpp Normal file
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@ -0,0 +1,85 @@
/* ----------------------------------------------------------------------------
* 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 <gtsam/nonlinear/Marginals.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("pose3Localizationexample.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
noiseModel::Diagonal::shared_ptr priorModel = //
noiseModel::Diagonal::Variances((Vector(6) << 1e-6, 1e-6, 1e-6, 1e-4, 1e-4, 1e-4).finished());
Key firstKey = 0;
for(const Values::ConstKeyValuePair& key_value: *initial) {
std::cout << "Adding prior to g2o file " << std::endl;
firstKey = key_value.key;
graph->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(*graph, *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;
NonlinearFactorGraph::shared_ptr graphNoKernel;
Values::shared_ptr initial2;
boost::tie(graphNoKernel, initial2) = readG2o(g2oFile);
writeG2o(*graphNoKernel, result, outputFile);
std::cout << "done! " << std::endl;
}
// Calculate and print marginal covariances for all variables
Marginals marginals(*graph, result);
for(const auto& key_value: result) {
auto p = dynamic_cast<const GenericValue<Pose3>*>(&key_value.value);
if (!p) continue;
std::cout << marginals.marginalCovariance(key_value.key) << endl;
}
return 0;
}

10
examples/Pose3SLAMExample_g2o.cpp
View File

@ -41,21 +41,20 @@ int main(const int argc, const char *argv[]) {
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).finished());
Key firstKey = 0;
for(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));
graph->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);
GaussNewtonOptimizer optimizer(*graph, *initial, params);
Values result = optimizer.optimize();
std::cout << "Optimization complete" << std::endl;
@ -67,7 +66,10 @@ int main(const int argc, const char *argv[]) {
} 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;

10
examples/Pose3SLAMExample_initializePose3Chordal.cpp
View File

@ -41,19 +41,18 @@ int main(const int argc, const char *argv[]) {
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).finished());
Key firstKey = 0;
for(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));
graph->add(PriorFactor<Pose3>(firstKey, Pose3(), priorModel));
break;
}
std::cout << "Initializing Pose3 - chordal relaxation" << std::endl;
Values initialization = InitializePose3::initialize(graphWithPrior);
Values initialization = InitializePose3::initialize(*graph);
std::cout << "done!" << std::endl;
if (argc < 3) {
@ -61,7 +60,10 @@ int main(const int argc, const char *argv[]) {
} else {
const string outputFile = argv[2];
std::cout << "Writing results to file: " << outputFile << std::endl;
writeG2o(*graph, initialization, outputFile);
NonlinearFactorGraph::shared_ptr graphNoKernel;
Values::shared_ptr initial2;
boost::tie(graphNoKernel, initial2) = readG2o(g2oFile);
writeG2o(*graphNoKernel, initialization, outputFile);
std::cout << "done! " << std::endl;
}
return 0;

10
examples/Pose3SLAMExample_initializePose3Gradient.cpp
View File

@ -41,20 +41,19 @@ int main(const int argc, const char *argv[]) {
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).finished());
Key firstKey = 0;
for(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));
graph->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);
Values initialization = InitializePose3::initialize(*graph, *initial, useGradient);
std::cout << "done!" << std::endl;
std::cout << "initial error=" <<graph->error(*initial)<< std::endl;
@ -65,7 +64,10 @@ int main(const int argc, const char *argv[]) {
} else {
const string outputFile = argv[2];
std::cout << "Writing results to file: " << outputFile << std::endl;
writeG2o(*graph, initialization, outputFile);
NonlinearFactorGraph::shared_ptr graphNoKernel;
Values::shared_ptr initial2;
boost::tie(graphNoKernel, initial2) = readG2o(g2oFile);
writeG2o(*graphNoKernel, initialization, outputFile);
std::cout << "done! " << std::endl;
}
return 0;

2
examples/SFMExample_SmartFactor.cpp
View File

@ -117,7 +117,7 @@ int main(int argc, char* argv[]) {
// The output of point() is in boost::optional<Point3>, as sometimes
// the triangulation operation inside smart factor will encounter degeneracy.
boost::optional<Point3> point = smart->point(result);
if (point) // ignore if boost::optional return NULL
if (point) // ignore if boost::optional return nullptr
landmark_result.insert(j, *point);
}
}

2
examples/SFMExample_SmartFactorPCG.cpp
View File

@ -114,7 +114,7 @@ int main(int argc, char* argv[]) {
boost::dynamic_pointer_cast<SmartFactor>(graph[j]);
if (smart) {
boost::optional<Point3> point = smart->point(result);
if (point) // ignore if boost::optional return NULL
if (point) // ignore if boost::optional return nullptr
landmark_result.insert(j, *point);
}
}

6
gtsam.h
View File

@ -1563,17 +1563,15 @@ virtual class Robust : gtsam::noiseModel::Base {
#include <gtsam/linear/Sampler.h>
class Sampler {
//Constructors
// Constructors
Sampler(gtsam::noiseModel::Diagonal* model, int seed);
Sampler(Vector sigmas, int seed);
Sampler(int seed);
//Standard Interface
// Standard Interface
size_t dim() const;
Vector sigmas() const;
gtsam::noiseModel::Diagonal* model() const;
Vector sample();
Vector sampleNewModel(gtsam::noiseModel::Diagonal* model);
};
#include <gtsam/linear/VectorValues.h>

26
gtsam/base/OptionalJacobian.h
View File

@ -55,7 +55,7 @@ private:
}
// Private and very dangerous constructor straight from memory
OptionalJacobian(double* data) : map_(NULL) {
OptionalJacobian(double* data) : map_(nullptr) {
if (data) usurp(data);
}
@ -66,25 +66,25 @@ public:
/// Default constructor acts like boost::none
OptionalJacobian() :
map_(NULL) {
map_(nullptr) {
}
/// Constructor that will usurp data of a fixed-size matrix
OptionalJacobian(Jacobian& fixed) :
map_(NULL) {
map_(nullptr) {
usurp(fixed.data());
}
/// Constructor that will usurp data of a fixed-size matrix, pointer version
OptionalJacobian(Jacobian* fixedPtr) :
map_(NULL) {
map_(nullptr) {
if (fixedPtr)
usurp(fixedPtr->data());
}
/// Constructor that will resize a dynamic matrix (unless already correct)
OptionalJacobian(Eigen::MatrixXd& dynamic) :
map_(NULL) {
map_(nullptr) {
dynamic.resize(Rows, Cols); // no malloc if correct size
usurp(dynamic.data());
}
@ -93,12 +93,12 @@ public:
/// Constructor with boost::none just makes empty
OptionalJacobian(boost::none_t /*none*/) :
map_(NULL) {
map_(nullptr) {
}
/// Constructor compatible with old-style derivatives
OptionalJacobian(const boost::optional<Eigen::MatrixXd&> optional) :
map_(NULL) {
map_(nullptr) {
if (optional) {
optional->resize(Rows, Cols);
usurp(optional->data());
@ -110,11 +110,11 @@ public:
/// Constructor that will usurp data of a block expression
/// TODO(frank): unfortunately using a Map makes usurping non-contiguous memory impossible
// template <typename Derived, bool InnerPanel>
// OptionalJacobian(Eigen::Block<Derived,Rows,Cols,InnerPanel> block) : map_(NULL) { ?? }
// OptionalJacobian(Eigen::Block<Derived,Rows,Cols,InnerPanel> block) : map_(nullptr) { ?? }
/// Return true is allocated, false if default constructor was used
operator bool() const {
return map_.data() != NULL;
return map_.data() != nullptr;
}
/// De-reference, like boost optional
@ -173,7 +173,7 @@ public:
/// Default constructor acts like boost::none
OptionalJacobian() :
pointer_(NULL) {
pointer_(nullptr) {
}
/// Construct from pointer to dynamic matrix
@ -186,12 +186,12 @@ public:
/// Constructor with boost::none just makes empty
OptionalJacobian(boost::none_t /*none*/) :
pointer_(NULL) {
pointer_(nullptr) {
}
/// Constructor compatible with old-style derivatives
OptionalJacobian(const boost::optional<Eigen::MatrixXd&> optional) :
pointer_(NULL) {
pointer_(nullptr) {
if (optional) pointer_ = &(*optional);
}
@ -199,7 +199,7 @@ public:
/// Return true is allocated, false if default constructor was used
operator bool() const {
return pointer_!=NULL;
return pointer_!=nullptr;
}
/// De-reference, like boost optional

12
gtsam/base/treeTraversal/parallelTraversalTasks.h
View File

@ -53,7 +53,7 @@ namespace gtsam {
// Run the post-order visitor
(void) visitorPost(treeNode, *myData);
return NULL;
return nullptr;
}
};
@ -88,7 +88,7 @@ namespace gtsam {
{
// Run the post-order visitor since this task was recycled to run the post-order visitor
(void) visitorPost(treeNode, *myData);
return NULL;
return nullptr;
}
else
{
@ -129,14 +129,14 @@ namespace gtsam {
{
// Run the post-order visitor in this task if we have no children
(void) visitorPost(treeNode, *myData);
return NULL;
return nullptr;
}
}
else
{
// Process this node and its children in this task
processNodeRecursively(treeNode, *myData);
return NULL;
return nullptr;
}
}
}
@ -184,8 +184,8 @@ namespace gtsam {
set_ref_count(1 + (int) roots.size());
// Spawn tasks
spawn_and_wait_for_all(tasks);
// Return NULL
return NULL;
// Return nullptr
return nullptr;
}
};

2
gtsam/discrete/AlgebraicDecisionTree.h
View File

@ -101,7 +101,7 @@ namespace gtsam {
/** Create a new function splitting on a variable */
template<typename Iterator>
AlgebraicDecisionTree(Iterator begin, Iterator end, const L& label) :
Super(NULL) {
Super(nullptr) {
this->root_ = compose(begin, end, label);
}

2
gtsam/discrete/DiscreteFactorGraph.cpp
View File

@ -71,7 +71,7 @@ namespace gtsam {
for (size_t i = 0; i < factors_.size(); i++) {
std::stringstream ss;
ss << "factor " << i << ": ";
if (factors_[i] != NULL) factors_[i]->print(ss.str(), formatter);
if (factors_[i] != nullptr) factors_[i]->print(ss.str(), formatter);
}
}

2
gtsam/geometry/tests/testEssentialMatrix.cpp
View File

@ -159,7 +159,7 @@ TEST (EssentialMatrix, rotate) {
Matrix actH1, actH2;
try {
bodyE.rotate(cRb, actH1, actH2);
} catch (exception e) {
} catch (exception& e) {
} // avoid exception
Matrix expH1 = numericalDerivative21(rotate_, bodyE, cRb), //
expH2 = numericalDerivative22(rotate_, bodyE, cRb);

2
gtsam/geometry/tests/testOrientedPlane3.cpp
View File

@ -183,7 +183,7 @@ TEST (OrientedPlane3, jacobian_retract) {
/* ************************************************************************* */
int main() {
srand(time(NULL));
srand(time(nullptr));
TestResult tr;
return TestRegistry::runAllTests(tr);
}

7
gtsam/geometry/tests/testPose3.cpp
View File

@ -1008,7 +1008,14 @@ TEST(Pose3, print) {
// Generate the expected output
std::stringstream expected;
Point3 translation(1, 2, 3);
#ifdef GTSAM_TYPEDEF_POINTS_TO_VECTORS
expected << "1\n"
"2\n"
"3;\n";
#else
expected << '[' << translation.x() << ", " << translation.y() << ", " << translation.z() << "]\';";
#endif
// reset cout to the original stream
std::cout.rdbuf(oldbuf);

2
gtsam/geometry/tests/testUnit3.cpp
View File

@ -495,7 +495,7 @@ TEST(actualH, Serialization) {
/* ************************************************************************* */
int main() {
srand(time(NULL));
srand(time(nullptr));
TestResult tr;
return TestRegistry::runAllTests(tr);
}

4
gtsam/inference/BayesTree-inst.h
View File

@ -125,7 +125,7 @@ namespace gtsam {
void BayesTree<CLIQUE>::addClique(const sharedClique& clique, const sharedClique& parent_clique) {
for(Key j: clique->conditional()->frontals())
nodes_[j] = clique;
if (parent_clique != NULL) {
if (parent_clique != nullptr) {
clique->parent_ = parent_clique;
parent_clique->children.push_back(clique);
} else {
@ -430,7 +430,7 @@ namespace gtsam {
template <class CLIQUE>
void BayesTree<CLIQUE>::removePath(sharedClique clique, BayesNetType* bn,
Cliques* orphans) {
// base case is NULL, if so we do nothing and return empties above
// base case is nullptr, if so we do nothing and return empties above
if (clique) {
// remove the clique from orphans in case it has been added earlier
orphans->remove(clique);

4
gtsam/inference/FactorGraph-inst.h
View File

@ -55,8 +55,8 @@ bool FactorGraph<FACTOR>::equals(const This& fg, double tol) const {
// check whether the factors are the same, in same order.
for (size_t i = 0; i < factors_.size(); i++) {
sharedFactor f1 = factors_[i], f2 = fg.factors_[i];
if (f1 == NULL && f2 == NULL) continue;
if (f1 == NULL || f2 == NULL) return false;
if (f1 == nullptr && f2 == nullptr) continue;
if (f1 == nullptr || f2 == nullptr) return false;
if (!f1->equals(*f2, tol)) return false;
}
return true;

2
gtsam/inference/FactorGraph.h
View File

@ -353,7 +353,7 @@ class FactorGraph {
*/
void resize(size_t size) { factors_.resize(size); }
/** delete factor without re-arranging indexes by inserting a NULL pointer
/** delete factor without re-arranging indexes by inserting a nullptr pointer
*/
void remove(size_t i) { factors_[i].reset(); }

4
gtsam/inference/Ordering.cpp
View File

@ -91,7 +91,7 @@ Ordering Ordering::ColamdConstrained(const VariableIndex& variableIndex,
assert((size_t)count == variableIndex.nEntries());
//double* knobs = NULL; /* colamd arg 6: parameters (uses defaults if NULL) */
//double* knobs = nullptr; /* colamd arg 6: parameters (uses defaults if nullptr) */
double knobs[CCOLAMD_KNOBS];
ccolamd_set_defaults(knobs);
knobs[CCOLAMD_DENSE_ROW] = -1;
@ -235,7 +235,7 @@ Ordering Ordering::Metis(const MetisIndex& met) {
int outputError;
outputError = METIS_NodeND(&size, &xadj[0], &adj[0], NULL, NULL, &perm[0],
outputError = METIS_NodeND(&size, &xadj[0], &adj[0], nullptr, nullptr, &perm[0],
&iperm[0]);
Ordering result;

2
gtsam/linear/GaussianBayesNet.h
View File

@ -143,7 +143,7 @@ namespace gtsam {
* allocateVectorValues */
VectorValues gradientAtZero() const;
/** Mahalanobis norm error. */
/** 0.5 * sum of squared Mahalanobis distances. */
double error(const VectorValues& x) const;
/**

2
gtsam/linear/GaussianBayesTree.h
View File

@ -106,7 +106,7 @@ namespace gtsam {
* @return A VectorValues storing the gradient. */
VectorValues gradientAtZero() const;
/** Mahalanobis norm error. */
/** 0.5 * sum of squared Mahalanobis distances. */
double error(const VectorValues& x) const;
/** Computes the determinant of a GassianBayesTree, as if the Bayes tree is reorganized into a

2
gtsam/linear/JacobianFactor.cpp
View File

@ -840,7 +840,7 @@ GaussianConditional::shared_ptr JacobianFactor::splitConditional(size_t nrFronta
if (!model_) {
throw std::invalid_argument(
"JacobianFactor::splitConditional cannot be given a NULL noise model");
"JacobianFactor::splitConditional cannot be given a nullptr noise model");
}
if (nrFrontals > size()) {

2
gtsam/linear/JacobianFactor.h
View File

@ -398,7 +398,7 @@ namespace gtsam {
* @param keys in some order
* @param diemnsions of the variables in same order
* @param m output dimension
* @param model noise model (default NULL)
* @param model noise model (default nullptr)
*/
template<class KEYS, class DIMENSIONS>
JacobianFactor(const KEYS& keys, const DIMENSIONS& dims, DenseIndex m,

16
gtsam/linear/LossFunctions.cpp
View File

@ -153,7 +153,7 @@ void Fair::print(const std::string &s="") const
bool Fair::equals(const Base &expected, double tol) const {
const Fair* p = dynamic_cast<const Fair*> (&expected);
if (p == NULL) return false;
if (p == nullptr) return false;
return std::abs(c_ - p->c_ ) < tol;
}
@ -190,7 +190,7 @@ void Huber::print(const std::string &s="") const {
bool Huber::equals(const Base &expected, double tol) const {
const Huber* p = dynamic_cast<const Huber*>(&expected);
if (p == NULL) return false;
if (p == nullptr) return false;
return std::abs(k_ - p->k_) < tol;
}
@ -223,7 +223,7 @@ void Cauchy::print(const std::string &s="") const {
bool Cauchy::equals(const Base &expected, double tol) const {
const Cauchy* p = dynamic_cast<const Cauchy*>(&expected);
if (p == NULL) return false;
if (p == nullptr) return false;
return std::abs(ksquared_ - p->ksquared_) < tol;
}
@ -266,7 +266,7 @@ void Tukey::print(const std::string &s="") const {
bool Tukey::equals(const Base &expected, double tol) const {
const Tukey* p = dynamic_cast<const Tukey*>(&expected);
if (p == NULL) return false;
if (p == nullptr) return false;
return std::abs(c_ - p->c_) < tol;
}
@ -296,7 +296,7 @@ void Welsch::print(const std::string &s="") const {
bool Welsch::equals(const Base &expected, double tol) const {
const Welsch* p = dynamic_cast<const Welsch*>(&expected);
if (p == NULL) return false;
if (p == nullptr) return false;
return std::abs(c_ - p->c_) < tol;
}
@ -330,7 +330,7 @@ void GemanMcClure::print(const std::string &s="") const {
bool GemanMcClure::equals(const Base &expected, double tol) const {
const GemanMcClure* p = dynamic_cast<const GemanMcClure*>(&expected);
if (p == NULL) return false;
if (p == nullptr) return false;
return std::abs(c_ - p->c_) < tol;
}
@ -372,7 +372,7 @@ void DCS::print(const std::string &s="") const {
bool DCS::equals(const Base &expected, double tol) const {
const DCS* p = dynamic_cast<const DCS*>(&expected);
if (p == NULL) return false;
if (p == nullptr) return false;
return std::abs(c_ - p->c_) < tol;
}
@ -411,7 +411,7 @@ void L2WithDeadZone::print(const std::string &s="") const {
bool L2WithDeadZone::equals(const Base &expected, double tol) const {
const L2WithDeadZone* p = dynamic_cast<const L2WithDeadZone*>(&expected);
if (p == NULL) return false;
if (p == nullptr) return false;
return std::abs(k_ - p->k_) < tol;
}

9
gtsam/linear/NoiseModel.cpp
View File

@ -25,7 +25,6 @@
#include <cmath>
#include <iostream>
#include <limits>
#include <random>
#include <stdexcept>
#include <typeinfo>
@ -134,7 +133,7 @@ void Gaussian::print(const string& name) const {
/* ************************************************************************* */
bool Gaussian::equals(const Base& expected, double tol) const {
const Gaussian* p = dynamic_cast<const Gaussian*> (&expected);
if (p == NULL) return false;
if (p == nullptr) return false;
if (typeid(*this) != typeid(*p)) return false;
//if (!sqrt_information_) return true; // ALEX todo;
return equal_with_abs_tol(R(), p->R(), sqrt(tol));
@ -157,7 +156,7 @@ Vector Gaussian::unwhiten(const Vector& v) const {
}
/* ************************************************************************* */
double Gaussian::Mahalanobis(const Vector& v) const {
double Gaussian::squaredMahalanobisDistance(const Vector& v) const {
// Note: for Diagonal, which does ediv_, will be correct for constraints
Vector w = whiten(v);
return w.dot(w);
@ -573,7 +572,7 @@ void Isotropic::print(const string& name) const {
}
/* ************************************************************************* */
double Isotropic::Mahalanobis(const Vector& v) const {
double Isotropic::squaredMahalanobisDistance(const Vector& v) const {
return v.dot(v) * invsigma_ * invsigma_;
}
@ -625,7 +624,7 @@ void Robust::print(const std::string& name) const {
bool Robust::equals(const Base& expected, double tol) const {
const Robust* p = dynamic_cast<const Robust*> (&expected);
if (p == NULL) return false;
if (p == nullptr) return false;
return noise_->equals(*p->noise_,tol) && robust_->equals(*p->robust_,tol);
}

23
gtsam/linear/NoiseModel.h
View File

@ -207,12 +207,25 @@ namespace gtsam {
virtual Vector unwhiten(const Vector& v) const;
/**
* Mahalanobis distance v'*R'*R*v = <R*v,R*v>
* Squared Mahalanobis distance v'*R'*R*v = <R*v,R*v>
*/
virtual double Mahalanobis(const Vector& v) const;
virtual double squaredMahalanobisDistance(const Vector& v) const;
/**
* Mahalanobis distance
*/
virtual double mahalanobisDistance(const Vector& v) const {
return std::sqrt(squaredMahalanobisDistance(v));
}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
virtual double Mahalanobis(const Vector& v) const {
return squaredMahalanobisDistance(v);
}
#endif
inline virtual double distance(const Vector& v) const {
return Mahalanobis(v);
return squaredMahalanobisDistance(v);
}
/**
@ -564,7 +577,7 @@ namespace gtsam {
}
virtual void print(const std::string& name) const;
virtual double Mahalanobis(const Vector& v) const;
virtual double squaredMahalanobisDistance(const Vector& v) const;
virtual Vector whiten(const Vector& v) const;
virtual Vector unwhiten(const Vector& v) const;
virtual Matrix Whiten(const Matrix& H) const;
@ -616,7 +629,7 @@ namespace gtsam {
virtual bool isUnit() const { return true; }
virtual void print(const std::string& name) const;
virtual double Mahalanobis(const Vector& v) const {return v.dot(v); }
virtual double squaredMahalanobisDistance(const Vector& v) const {return v.dot(v); }
virtual Vector whiten(const Vector& v) const { return v; }
virtual Vector unwhiten(const Vector& v) const { return v; }
virtual Matrix Whiten(const Matrix& H) const { return H; }

34
gtsam/linear/Sampler.cpp
View File

@ -11,6 +11,8 @@
/**
* @file Sampler.cpp
* @brief sampling from a diagonal NoiseModel
* @author Frank Dellaert
* @author Alex Cunningham
*/
@ -18,25 +20,16 @@
namespace gtsam {
/* ************************************************************************* */
Sampler::Sampler(const noiseModel::Diagonal::shared_ptr& model, int32_t seed)
: model_(model), generator_(static_cast<unsigned>(seed))
{
}
Sampler::Sampler(const noiseModel::Diagonal::shared_ptr& model,
uint_fast64_t seed)
: model_(model), generator_(seed) {}
/* ************************************************************************* */
Sampler::Sampler(const Vector& sigmas, int32_t seed)
: model_(noiseModel::Diagonal::Sigmas(sigmas, true)), generator_(static_cast<unsigned>(seed))
{
}
Sampler::Sampler(const Vector& sigmas, uint_fast64_t seed)
: model_(noiseModel::Diagonal::Sigmas(sigmas, true)), generator_(seed) {}
/* ************************************************************************* */
Sampler::Sampler(int32_t seed)
: generator_(static_cast<unsigned>(seed))
{
}
/* ************************************************************************* */
Vector Sampler::sampleDiagonal(const Vector& sigmas) {
Vector Sampler::sampleDiagonal(const Vector& sigmas) const {
size_t d = sigmas.size();
Vector result(d);
for (size_t i = 0; i < d; i++) {
@ -55,18 +48,23 @@ Vector Sampler::sampleDiagonal(const Vector& sigmas) {
}
/* ************************************************************************* */
Vector Sampler::sample() {
Vector Sampler::sample() const {
assert(model_.get());
const Vector& sigmas = model_->sigmas();
return sampleDiagonal(sigmas);
}
/* ************************************************************************* */
Vector Sampler::sampleNewModel(const noiseModel::Diagonal::shared_ptr& model) {
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
Sampler::Sampler(uint_fast64_t seed) : generator_(seed) {}
Vector Sampler::sampleNewModel(
const noiseModel::Diagonal::shared_ptr& model) const {
assert(model.get());
const Vector& sigmas = model->sigmas();
return sampleDiagonal(sigmas);
}
#endif
/* ************************************************************************* */
} // \namespace gtsam
} // namespace gtsam

79
gtsam/linear/Sampler.h
View File

@ -10,9 +10,9 @@
* -------------------------------------------------------------------------- */
/**
* @brief sampling that can be parameterized using a NoiseModel to generate samples from
* @file Sampler.h
* the given distribution
* @brief sampling from a NoiseModel
* @author Frank Dellaert
* @author Alex Cunningham
*/
@ -27,67 +27,74 @@ namespace gtsam {
/**
* Sampling structure that keeps internal random number generators for
* diagonal distributions specified by NoiseModel
*
* This is primarily to allow for variable seeds, and does roughly the same
* thing as sample() in NoiseModel.
*/
class GTSAM_EXPORT Sampler {
protected:
protected:
/** noiseModel created at generation */
noiseModel::Diagonal::shared_ptr model_;
/** generator */
std::mt19937_64 generator_;
mutable std::mt19937_64 generator_;
public:
public:
typedef boost::shared_ptr<Sampler> shared_ptr;
/// @name constructors
/// @{
/**
* Create a sampler for the distribution specified by a diagonal NoiseModel
* with a manually specified seed
*
* NOTE: do not use zero as a seed, it will break the generator
*/
Sampler(const noiseModel::Diagonal::shared_ptr& model, int32_t seed = 42u);
explicit Sampler(const noiseModel::Diagonal::shared_ptr& model,
uint_fast64_t seed = 42u);
/**
* Create a sampler for a distribution specified by a vector of sigmas directly
* Create a sampler for a distribution specified by a vector of sigmas
* directly
*
* NOTE: do not use zero as a seed, it will break the generator
*/
Sampler(const Vector& sigmas, int32_t seed = 42u);
explicit Sampler(const Vector& sigmas, uint_fast64_t seed = 42u);
/**
* Create a sampler without a given noisemodel - pass in to sample
*
* NOTE: do not use zero as a seed, it will break the generator
*/
Sampler(int32_t seed = 42u);
/// @}
/// @name access functions
/// @{
size_t dim() const {
assert(model_.get());
return model_->dim();
}
Vector sigmas() const {
assert(model_.get());
return model_->sigmas();
}
/** access functions */
size_t dim() const { assert(model_.get()); return model_->dim(); }
Vector sigmas() const { assert(model_.get()); return model_->sigmas(); }
const noiseModel::Diagonal::shared_ptr& model() const { return model_; }
/**
* sample from distribution
* NOTE: not const due to need to update the underlying generator
*/
Vector sample();
/// @}
/// @name basic functionality
/// @{
/**
* Sample from noisemodel passed in as an argument,
* can be used without having initialized a model for the system.
*
* NOTE: not const due to need to update the underlying generator
*/
Vector sampleNewModel(const noiseModel::Diagonal::shared_ptr& model);
/// sample from distribution
Vector sample() const;
protected:
/// @}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// @name Deprecated
/// @{
explicit Sampler(uint_fast64_t seed = 42u);
Vector sampleNewModel(const noiseModel::Diagonal::shared_ptr& model) const;
/// @}
#endif
protected:
/** given sigmas for a diagonal model, returns a sample */
Vector sampleDiagonal(const Vector& sigmas);
Vector sampleDiagonal(const Vector& sigmas) const;
};
} // \namespace gtsam
} // namespace gtsam

28
gtsam/linear/VectorValues.cpp
View File

@ -51,7 +51,11 @@ namespace gtsam {
Key key;
size_t n;
boost::tie(key, n) = v;
#ifdef TBB_GREATER_EQUAL_2020
values_.emplace(key, x.segment(j, n));
#else
values_.insert(std::make_pair(key, x.segment(j, n)));
#endif
j += n;
}
}
@ -60,7 +64,11 @@ namespace gtsam {
VectorValues::VectorValues(const Vector& x, const Scatter& scatter) {
size_t j = 0;
for (const SlotEntry& v : scatter) {
#ifdef TBB_GREATER_EQUAL_2020
values_.emplace(v.key, x.segment(j, v.dimension));
#else
values_.insert(std::make_pair(v.key, x.segment(j, v.dimension)));
#endif
j += v.dimension;
}
}
@ -70,7 +78,11 @@ namespace gtsam {
{
VectorValues result;
for(const KeyValuePair& v: other)
#ifdef TBB_GREATER_EQUAL_2020
result.values_.emplace(v.first, Vector::Zero(v.second.size()));
#else
result.values_.insert(std::make_pair(v.first, Vector::Zero(v.second.size())));
#endif
return result;
}
@ -86,7 +98,11 @@ namespace gtsam {
/* ************************************************************************* */
VectorValues::iterator VectorValues::emplace(Key j, const Vector& value) {
#ifdef TBB_GREATER_EQUAL_2020
std::pair<iterator, bool> result = values_.emplace(j, value);
#else
std::pair<iterator, bool> result = values_.insert(std::make_pair(j, value));
#endif
if(!result.second)
throw std::invalid_argument(
"Requested to emplace variable '" + DefaultKeyFormatter(j)
@ -266,7 +282,11 @@ namespace gtsam {
VectorValues result;
// The result.end() hint here should result in constant-time inserts
for(const_iterator j1 = begin(), j2 = c.begin(); j1 != end(); ++j1, ++j2)
#ifdef TBB_GREATER_EQUAL_2020
result.values_.emplace(j1->first, j1->second + j2->second);
#else
result.values_.insert(std::make_pair(j1->first, j1->second + j2->second));
#endif
return result;
}
@ -324,7 +344,11 @@ namespace gtsam {
VectorValues result;
// The result.end() hint here should result in constant-time inserts
for(const_iterator j1 = begin(), j2 = c.begin(); j1 != end(); ++j1, ++j2)
#ifdef TBB_GREATER_EQUAL_2020
result.values_.emplace(j1->first, j1->second - j2->second);
#else
result.values_.insert(std::make_pair(j1->first, j1->second - j2->second));
#endif
return result;
}
@ -340,7 +364,11 @@ namespace gtsam {
{
VectorValues result;
for(const VectorValues::KeyValuePair& key_v: v)
#ifdef TBB_GREATER_EQUAL_2020
result.values_.emplace(key_v.first, a * key_v.second);
#else
result.values_.insert(std::make_pair(key_v.first, a * key_v.second));
#endif
return result;
}

4
gtsam/linear/VectorValues.h
View File

@ -198,7 +198,11 @@ namespace gtsam {
* exist, it is inserted and an iterator pointing to the new element, along with 'true', is
* returned. */
std::pair<iterator, bool> tryInsert(Key j, const Vector& value) {
#ifdef TBB_GREATER_EQUAL_2020
return values_.emplace(j, value);
#else
return values_.insert(std::make_pair(j, value));
#endif
}
/** Erase the vector with the given key, or throw std::out_of_range if it does not exist */

2
gtsam/linear/tests/testJacobianFactor.cpp
View File

@ -253,7 +253,7 @@ TEST(JacobianFactor, error)
/* ************************************************************************* */
TEST(JacobianFactor, matrices_NULL)
{
// Make sure everything works with NULL noise model
// Make sure everything works with nullptr noise model
JacobianFactor factor(simple::terms, simple::b);
Matrix jacobianExpected(3, 9);

4
gtsam/linear/tests/testNoiseModel.cpp
View File

@ -68,10 +68,10 @@ TEST(NoiseModel, constructors)
for(Gaussian::shared_ptr mi: m)
EXPECT(assert_equal(unwhitened,mi->unwhiten(whitened)));
// test Mahalanobis distance
// test squared Mahalanobis distance
double distance = 5*5+10*10+15*15;
for(Gaussian::shared_ptr mi: m)
DOUBLES_EQUAL(distance,mi->Mahalanobis(unwhitened),1e-9);
DOUBLES_EQUAL(distance,mi->squaredMahalanobisDistance(unwhitened),1e-9);
// test R matrix
for(Gaussian::shared_ptr mi: m)

18
gtsam/linear/tests/testSampler.cpp
View File

@ -10,8 +10,10 @@
* -------------------------------------------------------------------------- */
/**
* @file testSampler
* @file testSampler.cpp
* @brief unit tests for Sampler class
* @author Alex Cunningham
* @author Frank Dellaert
*/
#include <CppUnitLite/TestHarness.h>
@ -22,14 +24,15 @@ using namespace gtsam;
const double tol = 1e-5;
static const Vector3 kSigmas(1.0, 0.1, 0.0);
/* ************************************************************************* */
TEST(testSampler, basic) {
Vector sigmas = Vector3(1.0, 0.1, 0.0);
noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(sigmas);
auto model = noiseModel::Diagonal::Sigmas(kSigmas);
char seed = 'A';
Sampler sampler1(model, seed), sampler2(model, 1), sampler3(model, 1);
EXPECT(assert_equal(sigmas, sampler1.sigmas()));
EXPECT(assert_equal(sigmas, sampler2.sigmas()));
EXPECT(assert_equal(kSigmas, sampler1.sigmas()));
EXPECT(assert_equal(kSigmas, sampler2.sigmas()));
EXPECT_LONGS_EQUAL(3, sampler1.dim());
EXPECT_LONGS_EQUAL(3, sampler2.dim());
Vector actual1 = sampler1.sample();
@ -38,5 +41,8 @@ TEST(testSampler, basic) {
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

2
gtsam/navigation/AHRSFactor.cpp
View File

@ -114,7 +114,7 @@ void AHRSFactor::print(const string& s,
//------------------------------------------------------------------------------
bool AHRSFactor::equals(const NonlinearFactor& other, double tol) const {
const This *e = dynamic_cast<const This*>(&other);
return e != NULL && Base::equals(*e, tol) && _PIM_.equals(e->_PIM_, tol);
return e != nullptr && Base::equals(*e, tol) && _PIM_.equals(e->_PIM_, tol);
}
//------------------------------------------------------------------------------

4
gtsam/navigation/AttitudeFactor.cpp
View File

@ -52,7 +52,7 @@ void Rot3AttitudeFactor::print(const string& s,
bool Rot3AttitudeFactor::equals(const NonlinearFactor& expected,
double tol) const {
const This* e = dynamic_cast<const This*>(&expected);
return e != NULL && Base::equals(*e, tol) && this->nZ_.equals(e->nZ_, tol)
return e != nullptr && Base::equals(*e, tol) && this->nZ_.equals(e->nZ_, tol)
&& this->bRef_.equals(e->bRef_, tol);
}
@ -69,7 +69,7 @@ void Pose3AttitudeFactor::print(const string& s,
bool Pose3AttitudeFactor::equals(const NonlinearFactor& expected,
double tol) const {
const This* e = dynamic_cast<const This*>(&expected);
return e != NULL && Base::equals(*e, tol) && this->nZ_.equals(e->nZ_, tol)
return e != nullptr && Base::equals(*e, tol) && this->nZ_.equals(e->nZ_, tol)
&& this->bRef_.equals(e->bRef_, tol);
}

2
gtsam/navigation/CombinedImuFactor.cpp
View File

@ -174,7 +174,7 @@ void CombinedImuFactor::print(const string& s,
//------------------------------------------------------------------------------
bool CombinedImuFactor::equals(const NonlinearFactor& other, double tol) const {
const This* e = dynamic_cast<const This*>(&other);
return e != NULL && Base::equals(*e, tol) && _PIM_.equals(e->_PIM_, tol);
return e != nullptr && Base::equals(*e, tol) && _PIM_.equals(e->_PIM_, tol);
}
//------------------------------------------------------------------------------

4
gtsam/navigation/GPSFactor.cpp
View File

@ -32,7 +32,7 @@ void GPSFactor::print(const string& s, const KeyFormatter& keyFormatter) const {
//***************************************************************************
bool GPSFactor::equals(const NonlinearFactor& expected, double tol) const {
const This* e = dynamic_cast<const This*>(&expected);
return e != NULL && Base::equals(*e, tol) && traits<Point3>::Equals(nT_, e->nT_, tol);
return e != nullptr && Base::equals(*e, tol) && traits<Point3>::Equals(nT_, e->nT_, tol);
}
//***************************************************************************
@ -73,7 +73,7 @@ void GPSFactor2::print(const string& s, const KeyFormatter& keyFormatter) const
//***************************************************************************
bool GPSFactor2::equals(const NonlinearFactor& expected, double tol) const {
const This* e = dynamic_cast<const This*>(&expected);
return e != NULL && Base::equals(*e, tol) &&
return e != nullptr && Base::equals(*e, tol) &&
traits<Point3>::Equals(nT_, e->nT_, tol);
}

2
gtsam/navigation/NavState.h
View File

@ -64,7 +64,7 @@ public:
R_(pose.rotation()), t_(pose.translation()), v_(v) {
}
/// Construct from SO(3) and R^6
NavState(const Matrix3& R, const Vector9 tv) :
NavState(const Matrix3& R, const Vector6& tv) :
R_(R), t_(tv.head<3>()), v_(tv.tail<3>()) {
}
/// Named constructor with derivatives

9
gtsam/navigation/PreintegrationParams.h
View File

@ -29,6 +29,13 @@ struct GTSAM_EXPORT PreintegrationParams: PreintegratedRotationParams {
bool use2ndOrderCoriolis; ///< Whether to use second order Coriolis integration
Vector3 n_gravity; ///< Gravity vector in nav frame
/// Default constructor for serialization only
PreintegrationParams()
: accelerometerCovariance(I_3x3),
integrationCovariance(I_3x3),
use2ndOrderCoriolis(false),
n_gravity(0, 0, -1) {}
/// The Params constructor insists on getting the navigation frame gravity vector
/// For convenience, two commonly used conventions are provided by named constructors below
PreintegrationParams(const Vector3& n_gravity)
@ -60,8 +67,6 @@ struct GTSAM_EXPORT PreintegrationParams: PreintegratedRotationParams {
bool getUse2ndOrderCoriolis() const { return use2ndOrderCoriolis; }
protected:
/// Default constructor for serialization only: uninitialized!
PreintegrationParams() {}
/** Serialization function */
friend class boost::serialization::access;

2
gtsam/navigation/ScenarioRunner.h
View File

@ -48,7 +48,7 @@ class GTSAM_EXPORT ScenarioRunner {
const Bias estimatedBias_;
// Create two samplers for acceleration and omega noise
mutable Sampler gyroSampler_, accSampler_;
Sampler gyroSampler_, accSampler_;
public:
ScenarioRunner(const Scenario& scenario, const SharedParams& p,

6
gtsam/navigation/tests/testImuFactorSerialization.cpp
View File

@ -64,9 +64,9 @@ TEST(ImuFactor, serialization) {
ImuFactor factor(1, 2, 3, 4, 5, pim);
EXPECT(equalsObj(factor));
EXPECT(equalsXML(factor));
EXPECT(equalsBinary(factor));
EXPECT(equalsObj<ImuFactor>(factor));
EXPECT(equalsXML<ImuFactor>(factor));
EXPECT(equalsBinary<ImuFactor>(factor));
}
/* ************************************************************************* */

2
gtsam/nonlinear/ISAM2Params.h
View File

@ -219,7 +219,7 @@ struct GTSAM_EXPORT ISAM2Params {
/// When you will be removing many factors, e.g. when using ISAM2 as a
/// fixed-lag smoother, enable this option to add factors in the first
/// available factor slots, to avoid accumulating NULL factor slots, at the
/// available factor slots, to avoid accumulating nullptr factor slots, at the
/// cost of having to search for slots every time a factor is added.
bool findUnusedFactorSlots;

6
gtsam/nonlinear/NonlinearEquality.h
View File

@ -175,6 +175,8 @@ public:
/// @}
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
private:
/** Serialization function */
@ -263,6 +265,8 @@ public:
traits<X>::Print(value_, "Value");
}
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
private:
/** Serialization function */
@ -327,6 +331,8 @@ public:
return traits<X>::Local(x1,x2);
}
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
private:
/** Serialization function */

6
gtsam/nonlinear/NonlinearFactorGraph.cpp
View File

@ -54,7 +54,7 @@ void NonlinearFactorGraph::print(const std::string& str, const KeyFormatter& key
for (size_t i = 0; i < factors_.size(); i++) {
stringstream ss;
ss << "Factor " << i << ": ";
if (factors_[i] != NULL) factors_[i]->print(ss.str(), keyFormatter);
if (factors_[i] != nullptr) factors_[i]->print(ss.str(), keyFormatter);
cout << endl;
}
}
@ -67,8 +67,8 @@ void NonlinearFactorGraph::printErrors(const Values& values, const std::string&
for (size_t i = 0; i < factors_.size(); i++) {
stringstream ss;
ss << "Factor " << i << ": ";
if (factors_[i] == NULL) {
cout << "NULL" << endl;
if (factors_[i] == nullptr) {
cout << "nullptr" << endl;
} else {
factors_[i]->print(ss.str(), keyFormatter);
cout << "error = " << factors_[i]->error(values) << endl;

2
gtsam/nonlinear/tests/testCallRecord.cpp
View File

@ -98,7 +98,7 @@ struct Record: public internal::CallRecordImplementor<Record, Cols> {
friend struct internal::CallRecordImplementor;
};
internal::JacobianMap & NJM= *static_cast<internal::JacobianMap *>(NULL);
internal::JacobianMap & NJM= *static_cast<internal::JacobianMap *>(nullptr);
/* ************************************************************************* */
typedef Eigen::Matrix<double, Eigen::Dynamic, Cols> DynRowMat;

7
gtsam/nonlinear/utilities.h
View File

@ -237,13 +237,16 @@ Values localToWorld(const Values& local, const Pose2& base,
// if value is a Pose2, compose it with base pose
Pose2 pose = local.at<Pose2>(key);
world.insert(key, base.compose(pose));
} catch (std::exception e1) {
} catch (const std::exception& e1) {
try {
// if value is a Point2, transform it from base pose
Point2 point = local.at<Point2>(key);
world.insert(key, base.transformFrom(point));
} catch (std::exception e2) {
} catch (const std::exception& e2) {
// if not Pose2 or Point2, do nothing
#ifndef NDEBUG
std::cerr << "Values[key] is neither Pose2 nor Point2, so skip" << std::endl;
#endif
}
}
}

2
gtsam/slam/AntiFactor.h
View File

@ -67,7 +67,7 @@ namespace gtsam {
/** 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->factor_->equals(*e->factor_, tol);
return e != nullptr && Base::equals(*e, tol) && this->factor_->equals(*e->factor_, tol);
}
/** implement functions needed to derive from Factor */

2
gtsam/slam/BetweenFactor.h
View File

@ -81,7 +81,7 @@ namespace gtsam {
/** 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) && traits<T>::Equals(this->measured_, e->measured_, tol);
return e != nullptr && Base::equals(*e, tol) && traits<T>::Equals(this->measured_, e->measured_, tol);
}
/** implement functions needed to derive from Factor */

2
gtsam/slam/EssentialMatrixConstraint.cpp
View File

@ -37,7 +37,7 @@ void EssentialMatrixConstraint::print(const std::string& s,
bool EssentialMatrixConstraint::equals(const NonlinearFactor& expected,
double tol) const {
const This *e = dynamic_cast<const This*>(&expected);
return e != NULL && Base::equals(*e, tol)
return e != nullptr && Base::equals(*e, tol)
&& this->measuredE_.equals(e->measuredE_, tol);
}

2
gtsam/slam/JacobianFactorSVD.h
View File

@ -65,7 +65,7 @@ public:
Base() {
size_t numKeys = Enull.rows() / ZDim;
size_t m2 = ZDim * numKeys - 3; // TODO: is this not just Enull.rows()?
// PLAIN NULL SPACE TRICK
// PLAIN nullptr SPACE TRICK
// Matrix Q = Enull * Enull.transpose();
// for(const KeyMatrixZD& it: Fblocks)
// QF.push_back(KeyMatrix(it.first, Q.block(0, 2 * j++, m2, 2) * it.second));

2
gtsam/slam/OrientedPlane3Factor.cpp
View File

@ -31,7 +31,7 @@ void OrientedPlane3DirectionPrior::print(const string& s,
bool OrientedPlane3DirectionPrior::equals(const NonlinearFactor& expected,
double tol) const {
const This* e = dynamic_cast<const This*>(&expected);
return e != NULL && Base::equals(*e, tol)
return e != nullptr && Base::equals(*e, tol)
&& this->measured_p_.equals(e->measured_p_, tol);
}

2
gtsam/slam/PoseRotationPrior.h
View File

@ -62,7 +62,7 @@ public:
/** equals specialized to this factor */
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) && measured_.equals(e->measured_, tol);
return e != nullptr && Base::equals(*e, tol) && measured_.equals(e->measured_, tol);
}
/** print contents */

2
gtsam/slam/PoseTranslationPrior.h
View File

@ -76,7 +76,7 @@ public:
/** equals specialized to this factor */
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) && traits<Translation>::Equals(measured_, e->measured_, tol);
return e != nullptr && Base::equals(*e, tol) && traits<Translation>::Equals(measured_, e->measured_, tol);
}
/** print contents */

2
gtsam/slam/PriorFactor.h
View File

@ -85,7 +85,7 @@ namespace gtsam {
/** 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) && traits<T>::Equals(prior_, e->prior_, tol);
return e != nullptr && Base::equals(*e, tol) && traits<T>::Equals(prior_, e->prior_, tol);
}
/** implement functions needed to derive from Factor */

6
gtsam/slam/dataset.cpp
View File

@ -252,7 +252,7 @@ GraphAndValues load2D(const string& filename, SharedNoiseModel model, Key maxID,
is.seekg(0, ios::beg);
// If asked, create a sampler with random number generator
Sampler sampler;
std::unique_ptr<Sampler> sampler;
if (addNoise) {
noiseModel::Diagonal::shared_ptr noise;
if (model)
@ -261,7 +261,7 @@ GraphAndValues load2D(const string& filename, SharedNoiseModel model, Key maxID,
throw invalid_argument(
"gtsam::load2D: invalid noise model for adding noise"
"(current version assumes diagonal noise model)!");
sampler = Sampler(noise);
sampler.reset(new Sampler(noise));
}
// Parse the pose constraints
@ -289,7 +289,7 @@ GraphAndValues load2D(const string& filename, SharedNoiseModel model, Key maxID,
model = modelInFile;
if (addNoise)
l1Xl2 = l1Xl2.retract(sampler.sample());
l1Xl2 = l1Xl2.retract(sampler->sample());
// Insert vertices if pure odometry file
if (!initial->exists(id1))

2
gtsam/slam/tests/testOrientedPlane3Factor.cpp
View File

@ -175,7 +175,7 @@ TEST( OrientedPlane3DirectionPrior, Constructor ) {
/* ************************************************************************* */
int main() {
srand(time(NULL));
srand(time(nullptr));
TestResult tr;
return TestRegistry::runAllTests(tr);
}

4
gtsam_unstable/base/BTree.h
View File

@ -72,7 +72,7 @@ namespace gtsam {
}; // Node
// We store a shared pointer to the root of the functional tree
// composed of Node classes. If root_==NULL, the tree is empty.
// composed of Node classes. If root_==nullptr, the tree is empty.
typedef boost::shared_ptr<const Node> sharedNode;
sharedNode root_;
@ -223,7 +223,7 @@ namespace gtsam {
/** Return height of the tree, 0 if empty */
size_t height() const {
return (root_ != NULL) ? root_->height_ : 0;
return (root_ != nullptr) ? root_->height_ : 0;
}
/** return size of the tree */

2
gtsam_unstable/linear/tests/testLinearEquality.cpp
View File

@ -136,7 +136,7 @@ TEST(LinearEquality, error)
/* ************************************************************************* */
TEST(LinearEquality, matrices_NULL)
{
// Make sure everything works with NULL noise model
// Make sure everything works with nullptr noise model
LinearEquality factor(simple::terms, simple::b, 0);
Matrix AExpected(3, 9);

6
gtsam_unstable/nonlinear/BatchFixedLagSmoother.cpp
View File

@ -39,7 +39,7 @@ bool BatchFixedLagSmoother::equals(const FixedLagSmoother& rhs,
double tol) const {
const BatchFixedLagSmoother* e =
dynamic_cast<const BatchFixedLagSmoother*>(&rhs);
return e != NULL && FixedLagSmoother::equals(*e, tol)
return e != nullptr && FixedLagSmoother::equals(*e, tol)
&& factors_.equals(e->factors_, tol) && theta_.equals(e->theta_, tol);
}
@ -145,7 +145,7 @@ void BatchFixedLagSmoother::removeFactors(
} else {
// TODO: Throw an error??
cout << "Attempting to remove a factor from slot " << slot
<< ", but it is already NULL." << endl;
<< ", but it is already nullptr." << endl;
}
}
}
@ -370,7 +370,7 @@ void BatchFixedLagSmoother::PrintSymbolicFactor(
cout << " " << DefaultKeyFormatter(key);
}
} else {
cout << " NULL";
cout << " nullptr";
}
cout << " )" << endl;
}

4
gtsam_unstable/nonlinear/ConcurrentBatchFilter.cpp
View File

@ -38,7 +38,7 @@ void ConcurrentBatchFilter::PrintNonlinearFactor(const NonlinearFactor::shared_p
}
std::cout << ")" << std::endl;
} else {
std::cout << "{ NULL }" << std::endl;
std::cout << "{ nullptr }" << std::endl;
}
}
@ -79,7 +79,7 @@ void ConcurrentBatchFilter::PrintLinearFactor(const GaussianFactor::shared_ptr&
}
std::cout << ")" << std::endl;
} else {
std::cout << "{ NULL }" << std::endl;
std::cout << "{ nullptr }" << std::endl;
}
}

4
gtsam_unstable/nonlinear/ConcurrentBatchSmoother.cpp
View File

@ -394,7 +394,7 @@ void ConcurrentBatchSmoother::PrintNonlinearFactor(const NonlinearFactor::shared
}
std::cout << ")" << std::endl;
} else {
std::cout << "{ NULL }" << std::endl;
std::cout << "{ nullptr }" << std::endl;
}
}
@ -408,7 +408,7 @@ void ConcurrentBatchSmoother::PrintLinearFactor(const GaussianFactor::shared_ptr
}
std::cout << ")" << std::endl;
} else {
std::cout << "{ NULL }" << std::endl;
std::cout << "{ nullptr }" << std::endl;
}
}

2
gtsam_unstable/nonlinear/IncrementalFixedLagSmoother.cpp
View File

@ -58,7 +58,7 @@ bool IncrementalFixedLagSmoother::equals(const FixedLagSmoother& rhs,
double tol) const {
const IncrementalFixedLagSmoother* e =
dynamic_cast<const IncrementalFixedLagSmoother*>(&rhs);
return e != NULL && FixedLagSmoother::equals(*e, tol)
return e != nullptr && FixedLagSmoother::equals(*e, tol)
&& isam_.equals(e->isam_, tol);
}

6
gtsam_unstable/partition/FindSeparator-inl.h
View File

@ -83,14 +83,14 @@ namespace gtsam { namespace partition {
graph_t *graph;
real_t *tpwgts2;
ctrl_t *ctrl;
ctrl = SetupCtrl(METIS_OP_OMETIS, options, 1, 3, NULL, NULL);
ctrl = SetupCtrl(METIS_OP_OMETIS, options, 1, 3, nullptr, nullptr);
ctrl->iptype = METIS_IPTYPE_GROW;
//if () == NULL)
//if () == nullptr)
// return METIS_ERROR_INPUT;
InitRandom(ctrl->seed);
graph = SetupGraph(ctrl, *nvtxs, 1, xadj, adjncy, vwgt, NULL, NULL);
graph = SetupGraph(ctrl, *nvtxs, 1, xadj, adjncy, vwgt, nullptr, nullptr);
AllocateWorkSpace(ctrl, graph);

2
gtsam_unstable/slam/AHRS.h
View File

@ -77,6 +77,8 @@ public:
void print(const std::string& s = "") const;
virtual ~AHRS();
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
} /* namespace gtsam */

2
gtsam_unstable/slam/BiasedGPSFactor.h
View File

@ -66,7 +66,7 @@ namespace gtsam {
/** 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) && traits<Point3>::Equals(this->measured_, e->measured_, tol);
return e != nullptr && Base::equals(*e, tol) && traits<Point3>::Equals(this->measured_, e->measured_, tol);
}
/** implement functions needed to derive from Factor */

2
gtsam_unstable/slam/EquivInertialNavFactor_GlobalVel.h
View File

@ -155,7 +155,7 @@ public:
/** 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)
return e != nullptr && Base::equals(*e, tol)
&& (delta_pos_in_t0_ - e->delta_pos_in_t0_).norm() < tol
&& (delta_vel_in_t0_ - e->delta_vel_in_t0_).norm() < tol
&& (delta_angles_ - e->delta_angles_).norm() < tol

2
gtsam_unstable/slam/EquivInertialNavFactor_GlobalVel_NoBias.h
View File

@ -153,7 +153,7 @@ public:
/** 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)
return e != nullptr && Base::equals(*e, tol)
&& (delta_pos_in_t0_ - e->delta_pos_in_t0_).norm() < tol
&& (delta_vel_in_t0_ - e->delta_vel_in_t0_).norm() < tol
&& (delta_angles_ - e->delta_angles_).norm() < tol

2
gtsam_unstable/slam/GaussMarkov1stOrderFactor.h
View File

@ -81,7 +81,7 @@ public:
/** 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);
return e != nullptr && Base::equals(*e, tol);
}
/** implement functions needed to derive from Factor */

2
gtsam_unstable/slam/InertialNavFactor_GlobalVelocity.h
View File

@ -135,7 +135,7 @@ public:
/** 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)
return e != nullptr && Base::equals(*e, tol)
&& (measurement_acc_ - e->measurement_acc_).norm() < tol
&& (measurement_gyro_ - e->measurement_gyro_).norm() < tol
&& (dt_ - e->dt_) < tol

2
gtsam_unstable/slam/PartialPriorFactor.h
View File

@ -100,7 +100,7 @@ namespace gtsam {
/** 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) &&
return e != nullptr && Base::equals(*e, tol) &&
gtsam::equal_with_abs_tol(this->prior_, e->prior_, tol) &&
this->mask_ == e->mask_;
}

2
gtsam_unstable/slam/PoseBetweenFactor.h
View File

@ -79,7 +79,7 @@ namespace gtsam {
/** equals */
virtual bool equals(const NonlinearFactor& expected, double tol=1e-9) const {
const This *e = dynamic_cast<const This*> (&expected);
return e != NULL
return e != nullptr
&& Base::equals(*e, tol)
&& this->measured_.equals(e->measured_, tol)
&& ((!body_P_sensor_ && !e->body_P_sensor_) || (body_P_sensor_ && e->body_P_sensor_ && body_P_sensor_->equals(*e->body_P_sensor_)));

2
gtsam_unstable/slam/PosePriorFactor.h
View File

@ -74,7 +74,7 @@ namespace gtsam {
/** equals */
virtual bool equals(const NonlinearFactor& expected, double tol=1e-9) const {
const This* e = dynamic_cast<const This*> (&expected);
return e != NULL
return e != nullptr
&& Base::equals(*e, tol)
&& this->prior_.equals(e->prior_, tol)
&& ((!body_P_sensor_ && !e->body_P_sensor_) || (body_P_sensor_ && e->body_P_sensor_ && body_P_sensor_->equals(*e->body_P_sensor_)));

2
gtsam_unstable/slam/RelativeElevationFactor.cpp
View File

@ -38,7 +38,7 @@ Vector RelativeElevationFactor::evaluateError(const Pose3& pose, const Point3& p
/* ************************************************************************* */
bool RelativeElevationFactor::equals(const NonlinearFactor& expected, double tol) const {
const This *e = dynamic_cast<const This*> (&expected);
return e != NULL && Base::equals(*e, tol) && std::abs(this->measured_ - e->measured_) < tol;
return e != nullptr && Base::equals(*e, tol) && std::abs(this->measured_ - e->measured_) < tol;
}
/* ************************************************************************* */

8
tests/testExtendedKalmanFilter.cpp
View File

@ -162,7 +162,7 @@ public:
/** Check if two factors are equal. Note type is IndexFactor and needs cast. */
virtual bool equals(const NonlinearFactor& f, double tol = 1e-9) const {
const This *e = dynamic_cast<const This*> (&f);
return (e != NULL) && (key1() == e->key1()) && (key2() == e->key2());
return (e != nullptr) && (key1() == e->key1()) && (key2() == e->key2());
}
/**
@ -196,7 +196,7 @@ public:
Vector b = -evaluateError(x1, x2, A1, A2);
SharedDiagonal constrained =
boost::dynamic_pointer_cast<noiseModel::Constrained>(this->noiseModel_);
if (constrained.get() != NULL) {
if (constrained.get() != nullptr) {
return JacobianFactor::shared_ptr(new JacobianFactor(key1(), A1, key2(),
A2, b, constrained));
}
@ -292,7 +292,7 @@ public:
/** Check if two factors are equal. Note type is IndexFactor and needs cast. */
virtual bool equals(const NonlinearFactor& f, double tol = 1e-9) const {
const This *e = dynamic_cast<const This*> (&f);
return (e != NULL) && Base::equals(f);
return (e != nullptr) && Base::equals(f);
}
/**
@ -325,7 +325,7 @@ public:
Vector b = -evaluateError(x1, A1);
SharedDiagonal constrained =
boost::dynamic_pointer_cast<noiseModel::Constrained>(this->noiseModel_);
if (constrained.get() != NULL) {
if (constrained.get() != nullptr) {
return JacobianFactor::shared_ptr(new JacobianFactor(key(), A1, b, constrained));
}
// "Whiten" the system before converting to a Gaussian Factor

128
tests/testNonlinearOptimizer.cpp
View File

@ -20,6 +20,7 @@
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/nonlinear/NonlinearConjugateGradientOptimizer.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <gtsam/nonlinear/DoglegOptimizer.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
@ -339,31 +340,136 @@ TEST_UNSAFE(NonlinearOptimizer, MoreOptimization) {
}
/* ************************************************************************* */
TEST(NonlinearOptimizer, MoreOptimizationWithHuber) {
TEST(NonlinearOptimizer, Pose2OptimizationWithHuberNoOutlier) {
NonlinearFactorGraph fg;
fg += PriorFactor<Pose2>(0, Pose2(0,0,0), noiseModel::Isotropic::Sigma(3,1));
fg += BetweenFactor<Pose2>(0, 1, Pose2(1,0,M_PI/2),
fg += BetweenFactor<Pose2>(0, 1, Pose2(1,1.1,M_PI/4),
noiseModel::Robust::Create(noiseModel::mEstimator::Huber::Create(2.0),
noiseModel::Isotropic::Sigma(3,1)));
fg += BetweenFactor<Pose2>(1, 2, Pose2(1,0,M_PI/2),
fg += BetweenFactor<Pose2>(0, 1, Pose2(1,0.9,M_PI/2),
noiseModel::Robust::Create(noiseModel::mEstimator::Huber::Create(3.0),
noiseModel::Isotropic::Sigma(3,1)));
Values init;
init.insert(0, Pose2(10,10,0));
init.insert(1, Pose2(1,0,M_PI));
init.insert(2, Pose2(1,1,-M_PI));
init.insert(0, Pose2(0,0,0));
init.insert(1, Pose2(0.961187, 0.99965, 1.1781));
Values expected;
expected.insert(0, Pose2(0,0,0));
expected.insert(1, Pose2(1,0,M_PI/2));
expected.insert(2, Pose2(1,1,M_PI));
expected.insert(1, Pose2(0.961187, 0.99965, 1.1781));
LevenbergMarquardtParams lm_params;
auto gn_result = GaussNewtonOptimizer(fg, init).optimize();
auto lm_result = LevenbergMarquardtOptimizer(fg, init, lm_params).optimize();
auto dl_result = DoglegOptimizer(fg, init).optimize();
EXPECT(assert_equal(expected, gn_result, 3e-2));
EXPECT(assert_equal(expected, lm_result, 3e-2));
EXPECT(assert_equal(expected, dl_result, 3e-2));
}
/* ************************************************************************* */
TEST(NonlinearOptimizer, Point2LinearOptimizationWithHuber) {
NonlinearFactorGraph fg;
fg += PriorFactor<Point2>(0, Point2(0,0), noiseModel::Isotropic::Sigma(2,0.01));
fg += BetweenFactor<Point2>(0, 1, Point2(1,1.8),
noiseModel::Robust::Create(noiseModel::mEstimator::Huber::Create(1.0),
noiseModel::Isotropic::Sigma(2,1)));
fg += BetweenFactor<Point2>(0, 1, Point2(1,0.9),
noiseModel::Robust::Create(noiseModel::mEstimator::Huber::Create(1.0),
noiseModel::Isotropic::Sigma(2,1)));
fg += BetweenFactor<Point2>(0, 1, Point2(1,90),
noiseModel::Robust::Create(noiseModel::mEstimator::Huber::Create(1.0),
noiseModel::Isotropic::Sigma(2,1)));
Values init;
init.insert(0, Point2(1,1));
init.insert(1, Point2(1,0));
Values expected;
expected.insert(0, Point2(0,0));
expected.insert(1, Point2(1,1.85));
LevenbergMarquardtParams params;
EXPECT(assert_equal(expected, GaussNewtonOptimizer(fg, init).optimize()));
EXPECT(assert_equal(expected, LevenbergMarquardtOptimizer(fg, init, params).optimize()));
EXPECT(assert_equal(expected, DoglegOptimizer(fg, init).optimize()));
auto gn_result = GaussNewtonOptimizer(fg, init).optimize();
auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize();
auto dl_result = DoglegOptimizer(fg, init).optimize();
EXPECT(assert_equal(expected, gn_result, 1e-4));
EXPECT(assert_equal(expected, lm_result, 1e-4));
EXPECT(assert_equal(expected, dl_result, 1e-4));
}
/* ************************************************************************* */
TEST(NonlinearOptimizer, Pose2OptimizationWithHuber) {
NonlinearFactorGraph fg;
fg += PriorFactor<Pose2>(0, Pose2(0,0, 0), noiseModel::Isotropic::Sigma(3,0.1));
fg += BetweenFactor<Pose2>(0, 1, Pose2(0,9, M_PI/2),
noiseModel::Robust::Create(noiseModel::mEstimator::Huber::Create(0.2),
noiseModel::Isotropic::Sigma(3,1)));
fg += BetweenFactor<Pose2>(0, 1, Pose2(0, 11, M_PI/2),
noiseModel::Robust::Create(noiseModel::mEstimator::Huber::Create(0.2),
noiseModel::Isotropic::Sigma(3,1)));
fg += BetweenFactor<Pose2>(0, 1, Pose2(0, 10, M_PI/2),
noiseModel::Robust::Create(noiseModel::mEstimator::Huber::Create(0.2),
noiseModel::Isotropic::Sigma(3,1)));
fg += BetweenFactor<Pose2>(0, 1, Pose2(0,9, 0),
noiseModel::Robust::Create(noiseModel::mEstimator::Huber::Create(0.2),
noiseModel::Isotropic::Sigma(3,1)));
Values init;
init.insert(0, Pose2(0, 0, 0));
init.insert(1, Pose2(0, 10, M_PI/4));
Values expected;
expected.insert(0, Pose2(0, 0, 0));
expected.insert(1, Pose2(0, 10, 1.45212));
LevenbergMarquardtParams params;
auto gn_result = GaussNewtonOptimizer(fg, init).optimize();
auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize();
auto dl_result = DoglegOptimizer(fg, init).optimize();
EXPECT(assert_equal(expected, gn_result, 1e-1));
EXPECT(assert_equal(expected, lm_result, 1e-1));
EXPECT(assert_equal(expected, dl_result, 1e-1));
}
/* ************************************************************************* */
TEST(NonlinearOptimizer, RobustMeanCalculation) {
NonlinearFactorGraph fg;
Values init;
Values expected;
auto huber = noiseModel::Robust::Create(noiseModel::mEstimator::Huber::Create(20),
noiseModel::Isotropic::Sigma(1, 1));
vector<double> pts{-10,-3,-1,1,3,10,1000};
for(auto pt : pts) {
fg += PriorFactor<double>(0, pt, huber);
}
init.insert(0, 100.0);
expected.insert(0, 3.33333333);
LevenbergMarquardtParams params;
auto gn_result = GaussNewtonOptimizer(fg, init).optimize();
auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize();
auto dl_result = DoglegOptimizer(fg, init).optimize();
EXPECT(assert_equal(expected, gn_result, tol));
EXPECT(assert_equal(expected, lm_result, tol));
EXPECT(assert_equal(expected, dl_result, tol));
}
/* ************************************************************************* */