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

release/4.3a0
Frank Dellaert 2019-05-17 22:19:27 -04:00
parent 970664b928 91e2ec930e
commit 67b65f9845
483 changed files with 8369 additions and 3971 deletions
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1
.gitignore vendored
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@ -1,4 +1,5 @@
/build*
/debug*
.idea
*.pyc
*.DS_Store

51
CMakeLists.txt
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@ -84,6 +84,12 @@ set(GTSAM_PYTHON_VERSION "Default" CACHE STRING "The version of python to build
if((GTSAM_INSTALL_MATLAB_TOOLBOX OR GTSAM_INSTALL_CYTHON_TOOLBOX) AND NOT GTSAM_BUILD_WRAP)
message(FATAL_ERROR "GTSAM_INSTALL_MATLAB_TOOLBOX or GTSAM_INSTALL_CYTHON_TOOLBOX is enabled, please also enable GTSAM_BUILD_WRAP")
endif()
if((GTSAM_INSTALL_MATLAB_TOOLBOX OR GTSAM_INSTALL_CYTHON_TOOLBOX) AND GTSAM_BUILD_TYPE_POSTFIXES)
set(CURRENT_POSTFIX ${CMAKE_${CMAKE_BUILD_TYPE}_POSTFIX})
if(NOT "${CURRENT_POSTFIX}" STREQUAL "")
message(FATAL_ERROR "Cannot use executable postfixes with the matlab or cython wrappers. Please disable GTSAM_BUILD_TYPE_POSTFIXES")
endif()
endif()
if(GTSAM_INSTALL_WRAP AND NOT GTSAM_BUILD_WRAP)
message(FATAL_ERROR "GTSAM_INSTALL_WRAP is enabled, please also enable GTSAM_BUILD_WRAP")
endif()
@ -130,6 +136,14 @@ if(MSVC)
endif()
endif()
# If building DLLs in MSVC, we need to avoid EIGEN_STATIC_ASSERT()
# or explicit instantiation will generate build errors.
# See: https://bitbucket.org/gtborg/gtsam/issues/417/fail-to-build-on-msvc-2017
#
if(MSVC AND BUILD_SHARED_LIBS)
list(APPEND GTSAM_COMPILE_DEFINITIONS_PUBLIC EIGEN_NO_STATIC_ASSERT)
endif()
# Store these in variables so they are automatically replicated in GTSAMConfig.cmake and such.
set(BOOST_FIND_MINIMUM_VERSION 1.43)
set(BOOST_FIND_MINIMUM_COMPONENTS serialization system filesystem thread program_options date_time timer chrono regex)
@ -142,22 +156,43 @@ if(NOT Boost_SERIALIZATION_LIBRARY OR NOT Boost_SYSTEM_LIBRARY OR NOT Boost_FILE
message(FATAL_ERROR "Missing required Boost components >= v1.43, please install/upgrade Boost or configure your search paths.")
endif()
option(GTSAM_DISABLE_NEW_TIMERS "Disables using Boost.chrono for timing" OFF)
# Allow for not using the timer libraries on boost < 1.48 (GTSAM timing code falls back to old timer library)
option(GTSAM_DISABLE_NEW_TIMERS "Disables using Boost.chrono for timing" OFF)
# JLBC: This was once updated to target-based names (Boost::xxx), but it caused
# problems with Boost versions newer than FindBoost.cmake was prepared to handle,
# so we downgraded this to classic filenames-based variables, and manually adding
# the target_include_directories(xxx ${Boost_INCLUDE_DIR})
set(GTSAM_BOOST_LIBRARIES
Boost::serialization
Boost::system
Boost::filesystem
Boost::thread
Boost::date_time
Boost::regex
optimized
${Boost_SERIALIZATION_LIBRARY_RELEASE}
${Boost_SYSTEM_LIBRARY_RELEASE}
${Boost_FILESYSTEM_LIBRARY_RELEASE}
${Boost_THREAD_LIBRARY_RELEASE}
${Boost_DATE_TIME_LIBRARY_RELEASE}
${Boost_REGEX_LIBRARY_RELEASE}
debug
${Boost_SERIALIZATION_LIBRARY_DEBUG}
${Boost_SYSTEM_LIBRARY_DEBUG}
${Boost_FILESYSTEM_LIBRARY_DEBUG}
${Boost_THREAD_LIBRARY_DEBUG}
${Boost_DATE_TIME_LIBRARY_DEBUG}
${Boost_REGEX_LIBRARY_DEBUG}
)
message(STATUS "GTSAM_BOOST_LIBRARIES: ${GTSAM_BOOST_LIBRARIES}")
if (GTSAM_DISABLE_NEW_TIMERS)
message("WARNING: GTSAM timing instrumentation manually disabled")
list(APPEND GTSAM_COMPILE_DEFINITIONS_PUBLIC DGTSAM_DISABLE_NEW_TIMERS)
else()
if(Boost_TIMER_LIBRARY)
list(APPEND GTSAM_BOOST_LIBRARIES Boost::timer Boost::chrono)
list(APPEND GTSAM_BOOST_LIBRARIES
optimized
${Boost_TIMER_LIBRARY_RELEASE}
${Boost_CHRONO_LIBRARY_RELEASE}
debug
${Boost_TIMER_LIBRARY_DEBUG}
${Boost_CHRONO_LIBRARY_DEBUG}
)
else()
list(APPEND GTSAM_BOOST_LIBRARIES rt) # When using the header-only boost timer library, need -lrt
message("WARNING: GTSAM timing instrumentation will use the older, less accurate, Boost timer library because boost older than 1.48 was found.")

2
CppUnitLite/CMakeLists.txt
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@ -6,7 +6,7 @@ file(GLOB cppunitlite_src "*.cpp")
add_library(CppUnitLite STATIC ${cppunitlite_src} ${cppunitlite_headers})
list(APPEND GTSAM_EXPORTED_TARGETS CppUnitLite)
set(GTSAM_EXPORTED_TARGETS "${GTSAM_EXPORTED_TARGETS}" PARENT_SCOPE)
target_link_libraries(CppUnitLite PUBLIC Boost::boost) # boost/lexical_cast.h
target_include_directories(CppUnitLite PUBLIC ${Boost_INCLUDE_DIR}) # boost/lexical_cast.h
gtsam_assign_source_folders("${cppunitlite_headers};${cppunitlite_src}") # MSVC project structure

27
INSTALL.md
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@ -166,3 +166,30 @@ Another useful debugging symbol is _GLIBCXX_DEBUG, which enables debug checks an
NOTE: The native Snow Leopard g++ compiler/library contains a bug that makes it impossible to use _GLIBCXX_DEBUG. MacPorts g++ compilers do work with it though.
NOTE: If _GLIBCXX_DEBUG is used to compile gtsam, anything that links against gtsam will need to be compiled with _GLIBCXX_DEBUG as well, due to the use of header-only Eigen.
## Installing MKL on Linux
Intel has a guide for installing MKL on Linux through APT repositories at <https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo>.
After following the instructions, add the following to your `~/.bashrc` (and afterwards, open a new terminal before compiling GTSAM):
`LD_PRELOAD` need only be set if you are building the cython wrapper to use GTSAM from python.
```sh
source /opt/intel/mkl/bin/mklvars.sh intel64
export LD_PRELOAD="$LD_PRELOAD:/opt/intel/mkl/lib/intel64/libmkl_core.so:/opt/intel/mkl/lib/intel64/libmkl_sequential.so"
```
To use MKL in GTSAM pass the flag `-DGTSAM_WITH_EIGEN_MKL=ON` to cmake.
The `LD_PRELOAD` fix seems to be related to a well known problem with MKL which leads to lots of undefined symbol errors, for example:
- <https://software.intel.com/en-us/forums/intel-math-kernel-library/topic/300857>
- <https://software.intel.com/en-us/forums/intel-distribution-for-python/topic/628976>
- <https://groups.google.com/a/continuum.io/forum/#!topic/anaconda/J3YGoef64z8>
Failing to specify `LD_PRELOAD` may lead to errors such as:
`ImportError: /opt/intel/mkl/lib/intel64/libmkl_vml_avx2.so: undefined symbol: mkl_serv_getenv`
or
`Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so.`
when importing GTSAM using the cython wrapper in python.

4
README.md
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@ -36,7 +36,9 @@ Prerequisites:
Optional prerequisites - used automatically if findable by CMake:
- [Intel Threaded Building Blocks (TBB)](http://www.threadingbuildingblocks.org/) (Ubuntu: `sudo apt-get install libtbb-dev`)
- [Intel Math Kernel Library (MKL)](http://software.intel.com/en-us/intel-mkl)
- [Intel Math Kernel Library (MKL)](http://software.intel.com/en-us/intel-mkl) (Ubuntu: [installing using APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo))
- See [INSTALL.md](INSTALL.md) for more installation information
- Note that MKL may not provide a speedup in all cases. Make sure to benchmark your problem with and without MKL.
GTSAM 4 Compatibility
---------------------

5
cmake/example_project/CMakeLists.txt
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@ -30,9 +30,12 @@ find_package(GTSAM REQUIRED) # Uses installed package
###################################################################################
# Build static library from common sources
set(CONVENIENCE_LIB_NAME ${PROJECT_NAME})
add_library(${CONVENIENCE_LIB_NAME} STATIC example/PrintExamples.h example/PrintExamples.cpp)
add_library(${CONVENIENCE_LIB_NAME} SHARED example/PrintExamples.h example/PrintExamples.cpp)
target_link_libraries(${CONVENIENCE_LIB_NAME} gtsam)
# Install library
install(TARGETS ${CONVENIENCE_LIB_NAME} LIBRARY DESTINATION lib ARCHIVE DESTINATION lib RUNTIME DESTINATION bin)
###################################################################################
# Build tests (CMake tracks the dependecy to link with GTSAM through our project's static library)
gtsamAddTestsGlob("example" "tests/test*.cpp" "" "${CONVENIENCE_LIB_NAME}")

32
cmake/example_project/README.md Normal file
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@ -0,0 +1,32 @@
# MATLAB Wrapper Example Project
This project serves as a lightweight example for demonstrating how to wrap C++ code in MATLAB using GTSAM.
## Compiling
We follow the regular build procedure inside the `example_project` directory:
```sh
mkdir build && cd build
cmake ..
make -j8
sudo make install
sudo ldconfig # ensures the shared object file generated is correctly loaded
```
## Usage
Now you can open MATLAB and add the `gtsam_toolbox` to the MATLAB path
```matlab
addpath('/usr/local/gtsam_toolbox')
```
At this point you are ready to run the example project. Starting from the `example_project` directory inside MATLAB, simply run code like regular MATLAB, e.g.
```matlab
pe = example.PrintExamples();
pe.sayHello();
pe.sayGoodbye();
```

3
cmake/example_project/example.h
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@ -18,9 +18,12 @@
// This is an interface file for automatic MATLAB wrapper generation. See
// gtsam.h for full documentation and more examples.
#include <example/PrintExamples.h>
namespace example {
class PrintExamples {
PrintExamples();
void sayHello() const;
void sayGoodbye() const;
};

1
cmake/example_project/example/PrintExamples.h
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@ -17,6 +17,7 @@
#pragma once
#include <iostream>
#include <string>
namespace example {

2
cython/README.md
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@ -62,7 +62,7 @@ Examples: noiseModel_Gaussian, noiseModel_mEstimator_Tukey
- Casting from a base class to a derive class must be done explicitly.
Examples:
```Python
noiseBase = factor.get_noiseModel()
noiseBase = factor.noiseModel()
noiseGaussian = dynamic_cast_noiseModel_Gaussian_noiseModel_Base(noiseBase)
```

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cython/gtsam/examples/DogLegOptimizerExample.py Normal file
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@ -0,0 +1,118 @@
"""
GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
Atlanta, Georgia 30332-0415
All Rights Reserved
See LICENSE for the license information
Example comparing DoglegOptimizer with Levenberg-Marquardt.
Author: Frank Dellaert
"""
# pylint: disable=no-member, invalid-name
import math
import argparse
import gtsam
import matplotlib.pyplot as plt
import numpy as np
def run(args):
"""Test Dogleg vs LM, inspired by issue #452."""
# print parameters
print("num samples = {}, deltaInitial = {}".format(
args.num_samples, args.delta))
# Ground truth solution
T11 = gtsam.Pose2(0, 0, 0)
T12 = gtsam.Pose2(1, 0, 0)
T21 = gtsam.Pose2(0, 1, 0)
T22 = gtsam.Pose2(1, 1, 0)
# Factor graph
graph = gtsam.NonlinearFactorGraph()
# Priors
prior = gtsam.noiseModel_Isotropic.Sigma(3, 1)
graph.add(gtsam.PriorFactorPose2(11, T11, prior))
graph.add(gtsam.PriorFactorPose2(21, T21, prior))
# Odometry
model = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.01, 0.01, 0.3]))
graph.add(gtsam.BetweenFactorPose2(11, 12, T11.between(T12), model))
graph.add(gtsam.BetweenFactorPose2(21, 22, T21.between(T22), model))
# Range
model_rho = gtsam.noiseModel_Isotropic.Sigma(1, 0.01)
graph.add(gtsam.RangeFactorPose2(12, 22, 1.0, model_rho))
params = gtsam.DoglegParams()
params.setDeltaInitial(args.delta) # default is 10
# Add progressively more noise to ground truth
sigmas = [0.01, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20]
n = len(sigmas)
p_dl, s_dl, p_lm, s_lm = [0]*n, [0]*n, [0]*n, [0]*n
for i, sigma in enumerate(sigmas):
dl_fails, lm_fails = 0, 0
# Attempt num_samples optimizations for both DL and LM
for _attempt in range(args.num_samples):
initial = gtsam.Values()
initial.insert(11, T11.retract(np.random.normal(0, sigma, 3)))
initial.insert(12, T12.retract(np.random.normal(0, sigma, 3)))
initial.insert(21, T21.retract(np.random.normal(0, sigma, 3)))
initial.insert(22, T22.retract(np.random.normal(0, sigma, 3)))
# Run dogleg optimizer
dl = gtsam.DoglegOptimizer(graph, initial, params)
result = dl.optimize()
dl_fails += graph.error(result) > 1e-9
# Run
lm = gtsam.LevenbergMarquardtOptimizer(graph, initial)
result = lm.optimize()
lm_fails += graph.error(result) > 1e-9
# Calculate Bayes estimate of success probability
# using a beta prior of alpha=0.5, beta=0.5
alpha, beta = 0.5, 0.5
v = args.num_samples+alpha+beta
p_dl[i] = (args.num_samples-dl_fails+alpha)/v
p_lm[i] = (args.num_samples-lm_fails+alpha)/v
def stddev(p):
"""Calculate standard deviation."""
return math.sqrt(p*(1-p)/(1+v))
s_dl[i] = stddev(p_dl[i])
s_lm[i] = stddev(p_lm[i])
fmt = "sigma= {}:\tDL success {:.2f}% +/- {:.2f}%, LM success {:.2f}% +/- {:.2f}%"
print(fmt.format(sigma,
100*p_dl[i], 100*s_dl[i],
100*p_lm[i], 100*s_lm[i]))
if args.plot:
fig, ax = plt.subplots()
dl_plot = plt.errorbar(sigmas, p_dl, yerr=s_dl, label="Dogleg")
lm_plot = plt.errorbar(sigmas, p_lm, yerr=s_lm, label="LM")
plt.title("Dogleg emprical success vs. LM")
plt.legend(handles=[dl_plot, lm_plot])
ax.set_xlim(0, sigmas[-1]+1)
ax.set_ylim(0, 1)
plt.show()
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Compare Dogleg and LM success rates")
parser.add_argument("-n", "--num_samples", type=int, default=1000,
help="Number of samples for each sigma")
parser.add_argument("-d", "--delta", type=float, default=10.0,
help="Initial delta for dogleg")
parser.add_argument("-p", "--plot", action="store_true",
help="Flag to plot results")
args = parser.parse_args()
run(args)

10
cython/gtsam/examples/PlanarManipulatorExample.py
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@ -115,7 +115,7 @@ class ThreeLinkArm(object):
l1Zl1 = expmap(0.0, 0.0, q[0])
l2Zl2 = expmap(0.0, self.L1, q[1])
l3Zl3 = expmap(0.0, 7.0, q[2])
l3Zl3 = expmap(0.0, self.L1+self.L2, q[2])
return compose(l1Zl1, l2Zl2, l3Zl3, self.sXt0)
def ik(self, sTt_desired, e=1e-9):
@ -297,12 +297,18 @@ def run_example():
""" Use trajectory interpolation and then trajectory tracking a la Murray
to move a 3-link arm on a straight line.
"""
# Create arm
arm = ThreeLinkArm()
# Get initial pose using forward kinematics
q = np.radians(vector3(30, -30, 45))
sTt_initial = arm.fk(q)
# Create interpolated trajectory in task space to desired goal pose
sTt_goal = Pose2(2.4, 4.3, math.radians(0))
poses = trajectory(sTt_initial, sTt_goal, 50)
# Setup figure and plot initial pose
fignum = 0
fig = plt.figure(fignum)
axes = fig.gca()
@ -310,6 +316,8 @@ def run_example():
axes.set_ylim(0, 10)
gtsam_plot.plot_pose2(fignum, arm.fk(q))
# For all poses in interpolated trajectory, calculate dq to move to next pose.
# We do this by calculating the local Jacobian J and doing dq = inv(J)*delta(sTt, pose).
for pose in poses:
sTt = arm.fk(q)
error = delta(sTt, pose)

4
cython/gtsam/tests/test_Pose3.py
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@ -30,9 +30,9 @@ class TestPose3(GtsamTestCase):
self.gtsamAssertEquals(actual, expected, 1e-6)
def test_transform_to(self):
"""Test transform_to method."""
"""Test transformTo method."""
transform = Pose3(Rot3.Rodrigues(0, 0, -1.570796), Point3(2, 4, 0))
actual = transform.transform_to(Point3(3, 2, 10))
actual = transform.transformTo(Point3(3, 2, 10))
expected = Point3(2, 1, 10)
self.gtsamAssertEquals(actual, expected, 1e-6)

38
cython/gtsam/tests/test_dsf_map.py Normal file
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@ -0,0 +1,38 @@
"""
GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
Atlanta, Georgia 30332-0415
All Rights Reserved
See LICENSE for the license information
Unit tests for Disjoint Set Forest.
Author: Frank Dellaert & Varun Agrawal
"""
# pylint: disable=invalid-name, no-name-in-module, no-member
from __future__ import print_function
import unittest
import gtsam
from gtsam.utils.test_case import GtsamTestCase
class TestDSFMap(GtsamTestCase):
"""Tests for DSFMap."""
def test_all(self):
"""Test everything in DFSMap."""
def key(index_pair):
return index_pair.i(), index_pair.j()
dsf = gtsam.DSFMapIndexPair()
pair1 = gtsam.IndexPair(1, 18)
self.assertEqual(key(dsf.find(pair1)), key(pair1))
pair2 = gtsam.IndexPair(2, 2)
dsf.merge(pair1, pair2)
self.assertTrue(dsf.find(pair1), dsf.find(pair1))
if __name__ == '__main__':
unittest.main()

20
examples/Data/HS21.QPS Normal file
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@ -0,0 +1,20 @@
NAME HS21
ROWS
N OBJ.FUNC
G R------1
COLUMNS
C------1 R------1 0.100000e+02
C------2 R------1 -.100000e+01
RHS
RHS OBJ.FUNC 0.100000e+03
RHS R------1 0.100000e+02
RANGES
BOUNDS
LO BOUNDS C------1 0.200000e+01
UP BOUNDS C------1 0.500000e+02
LO BOUNDS C------2 -.500000e+02
UP BOUNDS C------2 0.500000e+02
QUADOBJ
C------1 C------1 0.200000e-01
C------2 C------2 0.200000e+01
ENDATA

55
examples/Data/HS268.QPS Normal file
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@ -0,0 +1,55 @@
NAME HS268
ROWS
N OBJ.FUNC
G R------1
G R------2
G R------3
G R------4
G R------5
COLUMNS
C------1 OBJ.FUNC 0.183400e+05 R------1 -.100000e+01
C------1 R------2 0.100000e+02 R------3 -.800000e+01
C------1 R------4 0.800000e+01 R------5 -.400000e+01
C------2 OBJ.FUNC -.341980e+05 R------1 -.100000e+01
C------2 R------2 0.100000e+02 R------3 0.100000e+01
C------2 R------4 -.100000e+01 R------5 -.200000e+01
C------3 OBJ.FUNC 0.454200e+04 R------1 -.100000e+01
C------3 R------2 -.300000e+01 R------3 -.200000e+01
C------3 R------4 0.200000e+01 R------5 0.300000e+01
C------4 OBJ.FUNC 0.867200e+04 R------1 -.100000e+01
C------4 R------2 0.500000e+01 R------3 -.500000e+01
C------4 R------4 0.500000e+01 R------5 -.500000e+01
C------5 OBJ.FUNC 0.860000e+02 R------1 -.100000e+01
C------5 R------2 0.400000e+01 R------3 0.300000e+01
C------5 R------4 -.300000e+01 R------5 0.100000e+01
RHS
RHS OBJ.FUNC -.144630e+05
RHS R------1 -.500000e+01
RHS R------2 0.200000e+02
RHS R------3 -.400000e+02
RHS R------4 0.110000e+02
RHS R------5 -.300000e+02
RANGES
BOUNDS
FR BOUNDS C------1
FR BOUNDS C------2
FR BOUNDS C------3
FR BOUNDS C------4
FR BOUNDS C------5
QUADOBJ
C------1 C------1 0.203940e+05
C------1 C------2 -.249080e+05
C------1 C------3 -.202600e+04
C------1 C------4 0.389600e+04
C------1 C------5 0.658000e+03
C------2 C------2 0.418180e+05
C------2 C------3 -.346600e+04
C------2 C------4 -.982800e+04
C------2 C------5 -.372000e+03
C------3 C------3 0.351000e+04
C------3 C------4 0.217800e+04
C------3 C------5 -.348000e+03
C------4 C------4 0.303000e+04
C------4 C------5 -.440000e+02
C------5 C------5 0.540000e+02
ENDATA

20
examples/Data/HS35.QPS Normal file
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@ -0,0 +1,20 @@
NAME HS35
ROWS
N OBJ.FUNC
G R------1
COLUMNS
C------1 OBJ.FUNC -.800000e+01 R------1 -.100000e+01
C------2 OBJ.FUNC -.600000e+01 R------1 -.100000e+01
C------3 OBJ.FUNC -.400000e+01 R------1 -.200000e+01
RHS
RHS OBJ.FUNC -.900000e+01
RHS R------1 -.300000e+01
RANGES
BOUNDS
QUADOBJ
C------1 C------1 0.400000e+01
C------1 C------2 0.200000e+01
C------1 C------3 0.200000e+01
C------2 C------2 0.400000e+01
C------3 C------3 0.200000e+01
ENDATA

21
examples/Data/HS35MOD.QPS Normal file
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@ -0,0 +1,21 @@
NAME HS35MOD
ROWS
N OBJ.FUNC
G R------1
COLUMNS
C------1 OBJ.FUNC -.800000e+01 R------1 -.100000e+01
C------2 OBJ.FUNC -.600000e+01 R------1 -.100000e+01
C------3 OBJ.FUNC -.400000e+01 R------1 -.200000e+01
RHS
RHS OBJ.FUNC -.900000e+01
RHS R------1 -.300000e+01
RANGES
BOUNDS
FX BOUNDS C------2 0.500000e+00
QUADOBJ
C------1 C------1 0.400000e+01
C------1 C------2 0.200000e+01
C------1 C------3 0.200000e+01
C------2 C------2 0.400000e+01
C------3 C------3 0.200000e+01
ENDATA

33
examples/Data/HS51.QPS Normal file
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@ -0,0 +1,33 @@
NAME HS51
ROWS
N OBJ.FUNC
E R------1
E R------2
E R------3
COLUMNS
C------1 R------1 0.100000e+01
C------2 OBJ.FUNC -.400000e+01 R------1 0.300000e+01
C------2 R------3 0.100000e+01
C------3 OBJ.FUNC -.400000e+01 R------2 0.100000e+01
C------4 OBJ.FUNC -.200000e+01 R------2 0.100000e+01
C------5 OBJ.FUNC -.200000e+01 R------2 -.200000e+01
C------5 R------3 -.100000e+01
RHS
RHS OBJ.FUNC -.600000e+01
RHS R------1 0.400000e+01
RANGES
BOUNDS
FR BOUNDS C------1
FR BOUNDS C------2
FR BOUNDS C------3
FR BOUNDS C------4
FR BOUNDS C------5
QUADOBJ
C------1 C------1 0.200000e+01
C------1 C------2 -.200000e+01
C------2 C------2 0.400000e+01
C------2 C------3 0.200000e+01
C------3 C------3 0.200000e+01
C------4 C------4 0.200000e+01
C------5 C------5 0.200000e+01
ENDATA

32
examples/Data/HS52.QPS Normal file
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@ -0,0 +1,32 @@
NAME HS52
ROWS
N OBJ.FUNC
E R------1
E R------2
E R------3
COLUMNS
C------1 R------1 0.100000e+01
C------2 OBJ.FUNC -.400000e+01 R------1 0.300000e+01
C------2 R------3 0.100000e+01
C------3 OBJ.FUNC -.400000e+01 R------2 0.100000e+01
C------4 OBJ.FUNC -.200000e+01 R------2 0.100000e+01
C------5 OBJ.FUNC -.200000e+01 R------2 -.200000e+01
C------5 R------3 -.100000e+01
RHS
RHS OBJ.FUNC -.600000e+01
RANGES
BOUNDS
FR BOUNDS C------1
FR BOUNDS C------2
FR BOUNDS C------3
FR BOUNDS C------4
FR BOUNDS C------5
QUADOBJ
C------1 C------1 0.320000e+02
C------1 C------2 -.800000e+01
C------2 C------2 0.400000e+01
C------2 C------3 0.200000e+01
C------3 C------3 0.200000e+01
C------4 C------4 0.200000e+01
C------5 C------5 0.200000e+01
ENDATA

19
examples/Data/QPTEST.QPS Normal file
View File

@ -0,0 +1,19 @@
NAME QP example
ROWS
N obj
G r1
L r2
COLUMNS
c1 r1 2.0 r2 -1.0
c1 obj 1.5
c2 r1 1.0 r2 2.0
c2 obj -2.0
RHS
rhs1 r1 2.0 r2 6.0
BOUNDS
UP bnd1 c1 20.0
QUADOBJ
c1 c1 8.0
c1 c2 2.0
c2 c2 10.0
ENDATA

3414
examples/Data/randomGrid3D.xml Normal file
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File diff suppressed because it is too large Load Diff

169
examples/Data/toy3D.xml Normal file
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@ -0,0 +1,169 @@
<?xml version="1.0" encoding="UTF-8" standalone="yes" ?>
<!DOCTYPE boost_serialization>
<boost_serialization signature="serialization::archive" version="17">
<graph class_id="0" tracking_level="0" version="0">
<Base class_id="1" tracking_level="0" version="0">
<factors_ class_id="2" tracking_level="0" version="0">
<count>2</count>
<item_version>1</item_version>
<item class_id="3" tracking_level="0" version="1">
<px class_id="4" class_name="JacobianFactor" tracking_level="1" version="0" object_id="_0">
<Base class_id="5" tracking_level="0" version="0">
<Base class_id="6" tracking_level="0" version="0">
<keys_>
<count>2</count>
<item_version>0</item_version>
<item>0</item>
<item>1</item>
</keys_>
</Base>
</Base>
<Ab_ class_id="8" tracking_level="0" version="0">
<matrix_ class_id="9" tracking_level="0" version="0">
<rows>9</rows>
<cols>7</cols>
<data>
<item>1.22427709071730959e+01</item>
<item>1.51514613104920048e+01</item>
<item>3.60934366857813060e+00</item>
<item>-1.18407259026383116e+01</item>
<item>7.84826117220921216e+00</item>
<item>1.23509165494819051e+01</item>
<item>-6.09875015991639735e+00</item>
<item>6.16547190708139126e-01</item>
<item>3.94972084922329048e+00</item>
<item>-4.89208482920378174e+00</item>
<item>3.02091647632478866e+00</item>
<item>-8.95328692238917512e+00</item>
<item>7.89831607220345955e+00</item>
<item>-2.36793602009719084e+00</item>
<item>1.48517612051941725e+01</item>
<item>-3.97284286249233731e-01</item>
<item>-1.95744531643153863e+01</item>
<item>-3.85954855417462017e+00</item>
<item>4.79268277145419042e+00</item>
<item>-9.01707953629520453e+00</item>
<item>1.37848069005841385e+01</item>
<item>1.04829326688375950e+01</item>
<item>-5.00630568442241675e+00</item>
<item>4.70463561852773182e+00</item>
<item>-1.59179134598689274e+01</item>
<item>-2.04767784956723942e+00</item>
<item>9.54135497908261954e+00</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>-0.00000000000000000e+00</item>
<item>-1.76201757312015372e+00</item>
<item>1.98634190821282672e+01</item>
<item>1.52966546661624236e+00</item>
<item>1.94817649567575373e+01</item>
<item>1.39684693294110307e+00</item>
<item>4.30228460420588288e+00</item>
<item>1.76201757312015372e+00</item>
<item>-1.98634190821282672e+01</item>
<item>-1.52966546661624236e+00</item>
<item>-0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>4.16606867942304948e+00</item>
<item>1.86906420801308037e+00</item>
<item>-1.94717865319198360e+01</item>
<item>-1.94817649567575373e+01</item>
<item>-1.39684693294110307e+00</item>
<item>-4.30228460420588288e+00</item>
<item>-4.16606867942304948e+00</item>
<item>-1.86906420801308037e+00</item>
<item>1.94717865319198360e+01</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>1.00891462904330531e+01</item>
<item>-1.08132497987816869e+01</item>
<item>8.66487736568128497e+00</item>
<item>2.88370015604634311e+01</item>
<item>1.89391698948574643e+01</item>
<item>2.12398960190661290e+00</item>
<item>1.22150946112124039e+01</item>
<item>-2.33658532501596561e+01</item>
<item>1.51576204760307363e+01</item>
</data>
</matrix_>
<variableColOffsets_>
<count>4</count>
<item_version>0</item_version>
<item>0</item>
<item>3</item>
<item>6</item>
<item>7</item>
</variableColOffsets_>
<rowStart_>0</rowStart_>
<rowEnd_>9</rowEnd_>
<blockStart_>0</blockStart_>
</Ab_>
<model_ class_id="11" tracking_level="0" version="1">
<px class_id="-1"></px>
</model_>
</px>
</item>
<item>
<px class_id_reference="4" object_id="_1">
<Base>
<Base>
<keys_>
<count>2</count>
<item_version>0</item_version>
<item>0</item>
<item>1</item>
</keys_>
</Base>
</Base>
<Ab_>
<matrix_>
<rows>3</rows>
<cols>7</cols>
<data>
<item>1.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>1.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>1.00000000000000000e+00</item>
<item>1.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
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<item>0.00000000000000000e+00</item>
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<item>0.00000000000000000e+00</item>
<item>1.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
<item>0.00000000000000000e+00</item>
</data>
</matrix_>
<variableColOffsets_>
<count>4</count>
<item_version>0</item_version>
<item>0</item>
<item>3</item>
<item>6</item>
<item>7</item>
</variableColOffsets_>
<rowStart_>0</rowStart_>
<rowEnd_>3</rowEnd_>
<blockStart_>0</blockStart_>
</Ab_>
<model_>
<px class_id="-1"></px>
</model_>
</px>
</item>
</factors_>
</Base>
</graph>
</boost_serialization>

92
examples/InverseKinematicsExampleExpressions.cpp Normal file
View File

@ -0,0 +1,92 @@
/* ----------------------------------------------------------------------------
* 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 InverseKinematicsExampleExpressions.cpp
* @brief Implement inverse kinematics on a three-link arm using expressions.
* @date April 15, 2019
* @author Frank Dellaert
*/
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/ExpressionFactorGraph.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/Marginals.h>
#include <gtsam/nonlinear/expressions.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/expressions.h>
#include <cmath>
using namespace std;
using namespace gtsam;
// Scalar multiplication of a vector, with derivatives.
inline Vector3 scalarMultiply(const double& s, const Vector3& v,
OptionalJacobian<3, 1> Hs,
OptionalJacobian<3, 3> Hv) {
if (Hs) *Hs = v;
if (Hv) *Hv = s * I_3x3;
return s * v;
}
// Expression version of scalar product, using above function.
inline Vector3_ operator*(const Double_& s, const Vector3_& v) {
return Vector3_(&scalarMultiply, s, v);
}
// Expression version of Pose2::Expmap
inline Pose2_ Expmap(const Vector3_& xi) { return Pose2_(&Pose2::Expmap, xi); }
// Main function
int main(int argc, char** argv) {
// Three-link planar manipulator specification.
const double L1 = 3.5, L2 = 3.5, L3 = 2.5; // link lengths
const Pose2 sXt0(0, L1 + L2 + L3, M_PI / 2); // end-effector pose at rest
const Vector3 xi1(0, 0, 1), xi2(L1, 0, 1),
xi3(L1 + L2, 0, 1); // screw axes at rest
// Create Expressions for unknowns
using symbol_shorthand::Q;
Double_ q1(Q(1)), q2(Q(2)), q3(Q(3));
// Forward kinematics expression as product of exponentials
Pose2_ l1Zl1 = Expmap(q1 * Vector3_(xi1));
Pose2_ l2Zl2 = Expmap(q2 * Vector3_(xi2));
Pose2_ l3Zl3 = Expmap(q3 * Vector3_(xi3));
Pose2_ forward = compose(compose(l1Zl1, l2Zl2), compose(l3Zl3, Pose2_(sXt0)));
// Create a factor graph with a a single expression factor.
ExpressionFactorGraph graph;
Pose2 desiredEndEffectorPose(3, 2, 0);
auto model = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
graph.addExpressionFactor(forward, desiredEndEffectorPose, model);
// Create initial estimate
Values initial;
initial.insert(Q(1), 0.1);
initial.insert(Q(2), 0.2);
initial.insert(Q(3), 0.3);
initial.print("\nInitial Estimate:\n"); // print
GTSAM_PRINT(forward.value(initial));
// Optimize the initial values using a Levenberg-Marquardt nonlinear optimizer
LevenbergMarquardtParams params;
params.setlambdaInitial(1e6);
LevenbergMarquardtOptimizer optimizer(graph, initial, params);
Values result = optimizer.optimize();
result.print("Final Result:\n");
GTSAM_PRINT(forward.value(result));
return 0;
}

2
examples/Pose3SLAMExample_changeKeys.cpp
View File

@ -79,7 +79,7 @@ int main(const int argc, const char *argv[]) {
key2 = pose3Between->key2() - firstKey;
}
NonlinearFactor::shared_ptr simpleFactor(
new BetweenFactor<Pose3>(key1, key2, pose3Between->measured(), pose3Between->get_noiseModel()));
new BetweenFactor<Pose3>(key1, key2, pose3Between->measured(), pose3Between->noiseModel()));
simpleGraph.add(simpleFactor);
}
}

2
examples/SFMExampleExpressions.cpp
View File

@ -72,7 +72,7 @@ int main(int argc, char* argv[]) {
Point2 measurement = camera.project(points[j]);
// Below an expression for the prediction of the measurement:
Point3_ p('l', j);
Point2_ prediction = uncalibrate(cK, project(transform_to(x, p)));
Point2_ prediction = uncalibrate(cK, project(transformTo(x, p)));
// Again, here we use an ExpressionFactor
graph.addExpressionFactor(prediction, measurement, measurementNoise);
}

2
examples/SolverComparer.cpp
View File

@ -362,7 +362,7 @@ void runIncremental()
Rot2 measuredBearing = measured.bearing();
double measuredRange = measured.range();
newVariables.insert(lmKey,
pose.transform_from(measuredBearing.rotate(Point2(measuredRange, 0.0))));
pose.transformFrom(measuredBearing.rotate(Point2(measuredRange, 0.0))));
}
}
else

4
examples/StereoVOExample_large.cpp
View File

@ -89,8 +89,8 @@ int main(int argc, char** argv){
//if the landmark variable included in this factor has not yet been added to the initial variable value estimate, add it
if (!initial_estimate.exists(Symbol('l', l))) {
Pose3 camPose = initial_estimate.at<Pose3>(Symbol('x', x));
//transform_from() transforms the input Point3 from the camera pose space, camPose, to the global space
Point3 worldPoint = camPose.transform_from(Point3(X, Y, Z));
//transformFrom() transforms the input Point3 from the camera pose space, camPose, to the global space
Point3 worldPoint = camPose.transformFrom(Point3(X, Y, Z));
initial_estimate.insert(Symbol('l', l), worldPoint);
}
}

2
examples/elaboratePoint2KalmanFilter.cpp
View File

@ -140,7 +140,7 @@ int main() {
const GaussianConditional::shared_ptr& cg0 = linearBayesNet->back();
assert(cg0->nrFrontals() == 1);
assert(cg0->nrParents() == 0);
linearFactorGraph->add(0, cg0->get_R(), cg0->get_d() - cg0->get_R()*result[ordering->at(x1)], noiseModel::Diagonal::Sigmas(cg0->get_sigmas(), true));
linearFactorGraph->add(0, cg0->R(), cg0->d() - cg0->R()*result[ordering->at(x1)], noiseModel::Diagonal::Sigmas(cg0->get_sigmas(), true));
// Create the desired ordering
ordering = Ordering::shared_ptr(new Ordering);

67
gtsam.h
View File

@ -209,11 +209,60 @@ class KeyGroupMap {
bool insert2(size_t key, int val);
};
// Actually a FastSet<FactorIndex>
class FactorIndexSet {
FactorIndexSet();
FactorIndexSet(const gtsam::FactorIndexSet& set);
// common STL methods
size_t size() const;
bool empty() const;
void clear();
// structure specific methods
void insert(size_t factorIndex);
bool erase(size_t factorIndex); // returns true if value was removed
bool count(size_t factorIndex) const; // returns true if value exists
};
// Actually a vector<FactorIndex>
class FactorIndices {
FactorIndices();
FactorIndices(const gtsam::FactorIndices& other);
// common STL methods
size_t size() const;
bool empty() const;
void clear();
// structure specific methods
size_t at(size_t i) const;
size_t front() const;
size_t back() const;
void push_back(size_t factorIndex) const;
};
//*************************************************************************
// base
//*************************************************************************
/** gtsam namespace functions */
#include <gtsam/base/DSFMap.h>
class IndexPair {
IndexPair();
IndexPair(size_t i, size_t j);
size_t i() const;
size_t j() const;
};
template<KEY = {gtsam::IndexPair}>
class DSFMap {
DSFMap();
KEY find(const KEY& key) const;
void merge(const KEY& x, const KEY& y);
};
#include <gtsam/base/Matrix.h>
bool linear_independent(Matrix A, Matrix B, double tol);
@ -583,8 +632,8 @@ class Pose2 {
static Matrix wedge(double vx, double vy, double w);
// Group Actions on Point2
gtsam::Point2 transform_from(const gtsam::Point2& p) const;
gtsam::Point2 transform_to(const gtsam::Point2& p) const;
gtsam::Point2 transformFrom(const gtsam::Point2& p) const;
gtsam::Point2 transformTo(const gtsam::Point2& p) const;
// Standard Interface
double x() const;
@ -636,8 +685,8 @@ class Pose3 {
static Matrix wedge(double wx, double wy, double wz, double vx, double vy, double vz);
// Group Action on Point3
gtsam::Point3 transform_from(const gtsam::Point3& point) const;
gtsam::Point3 transform_to(const gtsam::Point3& point) const;
gtsam::Point3 transformFrom(const gtsam::Point3& point) const;
gtsam::Point3 transformTo(const gtsam::Point3& point) const;
// Standard Interface
gtsam::Rot3 rotation() const;
@ -646,7 +695,8 @@ class Pose3 {
double y() const;
double z() const;
Matrix matrix() const;
gtsam::Pose3 transform_to(const gtsam::Pose3& pose) const; // FIXME: shadows other transform_to()
gtsam::Pose3 transformPoseFrom(const gtsam::Pose3& pose) const;
gtsam::Pose3 transformPoseTo(const gtsam::Pose3& pose) const;
double range(const gtsam::Point3& point);
double range(const gtsam::Pose3& pose);
@ -1730,7 +1780,7 @@ virtual class SubgraphSolverParameters : gtsam::ConjugateGradientParameters {
virtual class SubgraphSolver {
SubgraphSolver(const gtsam::GaussianFactorGraph &A, const gtsam::SubgraphSolverParameters &parameters, const gtsam::Ordering& ordering);
SubgraphSolver(const gtsam::GaussianFactorGraph &Ab1, const gtsam::GaussianFactorGraph &Ab2, const gtsam::SubgraphSolverParameters &parameters, const gtsam::Ordering& ordering);
SubgraphSolver(const gtsam::GaussianFactorGraph &Ab1, const gtsam::GaussianFactorGraph* Ab2, const gtsam::SubgraphSolverParameters &parameters, const gtsam::Ordering& ordering);
gtsam::VectorValues optimize() const;
};
@ -1867,7 +1917,6 @@ virtual class NonlinearFactor {
#include <gtsam/nonlinear/NonlinearFactor.h>
virtual class NoiseModelFactor: gtsam::NonlinearFactor {
bool equals(const gtsam::NoiseModelFactor& other, double tol) const;
gtsam::noiseModel::Base* get_noiseModel() const; // deprecated by below
gtsam::noiseModel::Base* noiseModel() const;
Vector unwhitenedError(const gtsam::Values& x) const;
Vector whitenedError(const gtsam::Values& x) const;
@ -2091,7 +2140,7 @@ virtual class NonlinearOptimizer {
double error() const;
int iterations() const;
gtsam::Values values() const;
void iterate() const;
gtsam::GaussianFactorGraph* iterate() const;
};
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
@ -2209,8 +2258,6 @@ class ISAM2Result {
size_t getCliques() const;
};
class FactorIndices {};
class ISAM2 {
ISAM2();
ISAM2(const gtsam::ISAM2Params& params);

3
gtsam/CMakeLists.txt
View File

@ -101,7 +101,10 @@ message(STATUS "Building GTSAM - shared: ${BUILD_SHARED_LIBS}")
# BUILD_SHARED_LIBS automatically defines static/shared libs:
add_library(gtsam ${gtsam_srcs})
# Boost:
target_link_libraries(gtsam PUBLIC ${GTSAM_BOOST_LIBRARIES})
target_include_directories(gtsam PUBLIC ${Boost_INCLUDE_DIR})
# Other deps:
target_link_libraries(gtsam PUBLIC ${GTSAM_ADDITIONAL_LIBRARIES})
target_compile_definitions(gtsam PRIVATE ${GTSAM_COMPILE_DEFINITIONS_PRIVATE})
target_compile_definitions(gtsam PUBLIC ${GTSAM_COMPILE_DEFINITIONS_PUBLIC})

26
gtsam_unstable/base/DSFMap.h → gtsam/base/DSFMap.h
View File

@ -18,9 +18,9 @@
#pragma once
#include <cstdlib> // Provides size_t
#include <map>
#include <set>
#include <cstdlib> // Provides size_t
namespace gtsam {
@ -31,9 +31,7 @@ namespace gtsam {
*/
template <class KEY>
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_;
@ -62,8 +60,7 @@ protected:
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!
if (parent != it) parent = find_(parent); // not yet, recurse!
return parent;
}
@ -74,12 +71,10 @@ protected:
}
public:
typedef std::set<KEY> Set;
/// constructor
DSFMap() {
}
DSFMap() {}
/// Given key, find the representative key for the set in which it lives
inline KEY find(const KEY& key) const {
@ -89,12 +84,10 @@ public:
/// 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;
if (xRoot == yRoot) return;
// Merge sets
if (xRoot->second.rank_ < yRoot->second.rank_)
@ -117,7 +110,14 @@ public:
}
return sets;
}
};
}
/// Small utility class for representing a wrappable pairs of ints.
class IndexPair : public std::pair<size_t,size_t> {
public:
IndexPair(): std::pair<size_t,size_t>(0,0) {}
IndexPair(size_t i, size_t j) : std::pair<size_t,size_t>(i,j) {}
inline size_t i() const { return first; };
inline size_t j() const { return second; };
};
} // namespace gtsam

3
gtsam/base/SymmetricBlockMatrix.cpp
View File

@ -65,9 +65,10 @@ Matrix SymmetricBlockMatrix::block(DenseIndex I, DenseIndex J) const {
void SymmetricBlockMatrix::choleskyPartial(DenseIndex nFrontals) {
gttic(VerticalBlockMatrix_choleskyPartial);
DenseIndex topleft = variableColOffsets_[blockStart_];
if (!gtsam::choleskyPartial(matrix_, offset(nFrontals) - topleft, topleft))
if (!gtsam::choleskyPartial(matrix_, offset(nFrontals) - topleft, topleft)) {
throw CholeskyFailed();
}
}
/* ************************************************************************* */
VerticalBlockMatrix SymmetricBlockMatrix::split(DenseIndex nFrontals) {

42
gtsam/base/numericalDerivative.h
View File

@ -67,28 +67,29 @@ struct FixedSizeMatrix {
}
/**
* Numerically compute gradient of scalar function
* @brief Numerically compute gradient of scalar function
* @return n-dimensional gradient computed via central differencing
* Class X is the input argument
* The class X needs to have dim, expmap, logmap
* int N is the dimension of the X input value if variable dimension type but known at test time
*/
template<class X>
typename internal::FixedSizeMatrix<X>::type numericalGradient(boost::function<double(const X&)> h, const X& x,
double delta = 1e-5) {
template <class X, int N = traits<X>::dimension>
typename Eigen::Matrix<double, N, 1> numericalGradient(
boost::function<double(const X&)> h, const X& x, double delta = 1e-5) {
double factor = 1.0 / (2.0 * delta);
BOOST_STATIC_ASSERT_MSG(
(boost::is_base_of<manifold_tag, typename traits<X>::structure_category>::value),
"Template argument X must be a manifold type.");
static const int N = traits<X>::dimension;
BOOST_STATIC_ASSERT_MSG(N>0, "Template argument X must be fixed-size type.");
typedef typename traits<X>::TangentVector TangentX;
BOOST_STATIC_ASSERT_MSG(N>0, "Template argument X must be fixed-size type or N must be specified.");
// Prepare a tangent vector to perturb x with, only works for fixed size
TangentX d;
typename traits<X>::TangentVector d;
d.setZero();
Eigen::Matrix<double,N,1> g; g.setZero(); // Can be fixed size
Eigen::Matrix<double,N,1> g;
g.setZero();
for (int j = 0; j < N; j++) {
d(j) = delta;
double hxplus = h(traits<X>::Retract(x, d));
@ -108,37 +109,34 @@ typename internal::FixedSizeMatrix<X>::type numericalGradient(boost::function<do
* @param delta increment for numerical derivative
* Class Y is the output argument
* Class X is the input argument
* int N is the dimension of the X input value if variable dimension type but known at test time
* @return m*n Jacobian computed via central differencing
*/
template<class Y, class X>
template <class Y, class X, int N = traits<X>::dimension>
// TODO Should compute fixed-size matrix
typename internal::FixedSizeMatrix<Y,X>::type numericalDerivative11(boost::function<Y(const X&)> h, const X& x,
double delta = 1e-5) {
typename internal::FixedSizeMatrix<Y, X>::type numericalDerivative11(
boost::function<Y(const X&)> h, const X& x, double delta = 1e-5) {
typedef typename internal::FixedSizeMatrix<Y,X>::type Matrix;
BOOST_STATIC_ASSERT_MSG( (boost::is_base_of<gtsam::manifold_tag, typename traits<Y>::structure_category>::value),
"Template argument Y must be a manifold type.");
typedef traits<Y> TraitsY;
typedef typename TraitsY::TangentVector TangentY;
BOOST_STATIC_ASSERT_MSG( (boost::is_base_of<gtsam::manifold_tag, typename traits<X>::structure_category>::value),
"Template argument X must be a manifold type.");
static const int N = traits<X>::dimension;
BOOST_STATIC_ASSERT_MSG(N>0, "Template argument X must be fixed-size type.");
BOOST_STATIC_ASSERT_MSG(N>0, "Template argument X must be fixed-size type or N must be specified.");
typedef traits<X> TraitsX;
typedef typename TraitsX::TangentVector TangentX;
// get value at x, and corresponding chart
const Y hx = h(x);
// Bit of a hack for now to find number of rows
const TangentY zeroY = TraitsY::Local(hx, hx);
const typename TraitsY::TangentVector zeroY = TraitsY::Local(hx, hx);
const size_t m = zeroY.size();
// Prepare a tangent vector to perturb x with, only works for fixed size
TangentX dx;
Eigen::Matrix<double, N, 1> dx;
dx.setZero();
// Fill in Jacobian H
@ -146,9 +144,9 @@ typename internal::FixedSizeMatrix<Y,X>::type numericalDerivative11(boost::funct
const double factor = 1.0 / (2.0 * delta);
for (int j = 0; j < N; j++) {
dx(j) = delta;
const TangentY dy1 = TraitsY::Local(hx, h(TraitsX::Retract(x, dx)));
const auto dy1 = TraitsY::Local(hx, h(TraitsX::Retract(x, dx)));
dx(j) = -delta;
const TangentY dy2 = TraitsY::Local(hx, h(TraitsX::Retract(x, dx)));
const auto dy2 = TraitsY::Local(hx, h(TraitsX::Retract(x, dx)));
dx(j) = 0;
H.col(j) << (dy1 - dy2) * factor;
}

9
gtsam_unstable/base/tests/testDSFMap.cpp → gtsam/base/tests/testDSFMap.cpp
View File

@ -16,7 +16,7 @@
* @brief unit tests for DSFMap
*/
#include <gtsam_unstable/base/DSFMap.h>
#include <gtsam/base/DSFMap.h>
#include <boost/assign/std/list.hpp>
#include <boost/assign/std/set.hpp>
@ -115,7 +115,6 @@ TEST(DSFMap, mergePairwiseMatches2) {
TEST(DSFMap, sets){
// Create some "matches"
typedef pair<size_t,size_t> Match;
typedef pair<size_t, set<size_t> > key_pair;
list<Match> matches;
matches += Match(1,2), Match(2,3), Match(4,5), Match(4,6);
@ -140,6 +139,12 @@ TEST(DSFMap, sets){
EXPECT(s2 == sets[4]);
}
/* ************************************************************************* */
TEST(DSFMap, findIndexPair) {
DSFMap<IndexPair> dsf;
EXPECT(dsf.find(IndexPair(1,2))==IndexPair(1,2));
EXPECT(dsf.find(IndexPair(1,2)) != dsf.find(IndexPair(1,3)));
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */

3
gtsam/base/types.h
View File

@ -56,6 +56,9 @@ namespace gtsam {
/// Integer nonlinear key type
typedef std::uint64_t Key;
/// Integer nonlinear factor index type
typedef std::uint64_t FactorIndex;
/// The index type for Eigen objects
typedef ptrdiff_t DenseIndex;

3
gtsam/geometry/Cal3_S2.cpp
View File

@ -44,8 +44,7 @@ Cal3_S2::Cal3_S2(const std::string &path) :
if (infile)
infile >> fx_ >> fy_ >> s_ >> u0_ >> v0_;
else {
printf("Unable to load the calibration\n");
exit(0);
throw std::runtime_error("Cal3_S2: Unable to load the calibration");
}
infile.close();

6
gtsam/geometry/CalibratedCamera.cpp
View File

@ -107,7 +107,7 @@ Point2 PinholeBase::Project(const Unit3& pc, OptionalJacobian<2, 2> Dpoint) {
/* ************************************************************************* */
pair<Point2, bool> PinholeBase::projectSafe(const Point3& pw) const {
const Point3 pc = pose().transform_to(pw);
const Point3 pc = pose().transformTo(pw);
const Point2 pn = Project(pc);
return make_pair(pn, pc.z() > 0);
}
@ -116,8 +116,8 @@ pair<Point2, bool> PinholeBase::projectSafe(const Point3& pw) const {
Point2 PinholeBase::project2(const Point3& point, OptionalJacobian<2, 6> Dpose,
OptionalJacobian<2, 3> Dpoint) const {
Matrix3 Rt; // calculated by transform_to if needed
const Point3 q = pose().transform_to(point, boost::none, Dpoint ? &Rt : 0);
Matrix3 Rt; // calculated by transformTo if needed
const Point3 q = pose().transformTo(point, boost::none, Dpoint ? &Rt : 0);
#ifdef GTSAM_THROW_CHEIRALITY_EXCEPTION
if (q.z() <= 0)
throw CheiralityException();

2
gtsam/geometry/CalibratedCamera.h
View File

@ -359,7 +359,7 @@ public:
Dresult_ddepth ? &Dpoint_ddepth : 0);
Matrix33 Dresult_dpoint;
const Point3 result = pose().transform_from(point, Dresult_dpose,
const Point3 result = pose().transformFrom(point, Dresult_dpose,
(Dresult_ddepth ||
Dresult_dp) ? &Dresult_dpoint : 0);

6
gtsam/geometry/EssentialMatrix.cpp
View File

@ -52,13 +52,13 @@ void EssentialMatrix::print(const string& s) const {
}
/* ************************************************************************* */
Point3 EssentialMatrix::transform_to(const Point3& p, OptionalJacobian<3, 5> DE,
Point3 EssentialMatrix::transformTo(const Point3& p, OptionalJacobian<3, 5> DE,
OptionalJacobian<3, 3> Dpoint) const {
Pose3 pose(rotation(), direction().point3());
Matrix36 DE_;
Point3 q = pose.transform_to(p, DE ? &DE_ : 0, Dpoint);
Point3 q = pose.transformTo(p, DE ? &DE_ : 0, Dpoint);
if (DE) {
// DE returned by pose.transform_to is 3*6, but we need it to be 3*5
// DE returned by pose.transformTo is 3*6, but we need it to be 3*5
// The last 3 columns are derivative with respect to change in translation
// The derivative of translation with respect to a 2D sphere delta is 3*2 direction().basis()
// Duy made an educated guess that this needs to be rotated to the local frame

13
gtsam/geometry/EssentialMatrix.h
View File

@ -138,7 +138,7 @@ class GTSAM_EXPORT EssentialMatrix {
* @param Dpoint optional 3*3 Jacobian wrpt point
* @return point in pose coordinates
*/
Point3 transform_to(const Point3& p,
Point3 transformTo(const Point3& p,
OptionalJacobian<3, 5> DE = boost::none,
OptionalJacobian<3, 3> Dpoint = boost::none) const;
@ -176,6 +176,17 @@ class GTSAM_EXPORT EssentialMatrix {
/// @}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// @name Deprecated
/// @{
Point3 transform_to(const Point3& p,
OptionalJacobian<3, 5> DE = boost::none,
OptionalJacobian<3, 3> Dpoint = boost::none) const {
return transformTo(p, DE, Dpoint);
};
/// @}
#endif
private:
/// @name Advanced Interface
/// @{

2
gtsam/geometry/PinholePose.h
View File

@ -145,7 +145,7 @@ public:
(Dresult_dp || Dresult_dcal) ? &Dpoint_dpn : 0,
Dresult_ddepth ? &Dpoint_ddepth : 0);
Matrix33 Dresult_dpoint;
const Point3 result = pose().transform_from(point, Dresult_dpose,
const Point3 result = pose().transformFrom(point, Dresult_dpose,
(Dresult_ddepth ||
Dresult_dp ||
Dresult_dcal) ? &Dresult_dpoint : 0);

8
gtsam/geometry/Pose2.cpp
View File

@ -198,7 +198,7 @@ Pose2 Pose2::inverse() const {
/* ************************************************************************* */
// see doc/math.lyx, SE(2) section
Point2 Pose2::transform_to(const Point2& point,
Point2 Pose2::transformTo(const Point2& point,
OptionalJacobian<2, 3> Hpose, OptionalJacobian<2, 2> Hpoint) const {
OptionalJacobian<2, 2> Htranslation = Hpose.cols<2>(0);
OptionalJacobian<2, 1> Hrotation = Hpose.cols<1>(2);
@ -209,7 +209,7 @@ Point2 Pose2::transform_to(const Point2& point,
/* ************************************************************************* */
// see doc/math.lyx, SE(2) section
Point2 Pose2::transform_from(const Point2& point,
Point2 Pose2::transformFrom(const Point2& point,
OptionalJacobian<2, 3> Hpose, OptionalJacobian<2, 2> Hpoint) const {
OptionalJacobian<2, 2> Htranslation = Hpose.cols<2>(0);
OptionalJacobian<2, 1> Hrotation = Hpose.cols<1>(2);
@ -223,7 +223,7 @@ Rot2 Pose2::bearing(const Point2& point,
OptionalJacobian<1, 3> Hpose, OptionalJacobian<1, 2> Hpoint) const {
// make temporary matrices
Matrix23 D_d_pose; Matrix2 D_d_point;
Point2 d = transform_to(point, Hpose ? &D_d_pose : 0, Hpoint ? &D_d_point : 0);
Point2 d = transformTo(point, Hpose ? &D_d_pose : 0, Hpoint ? &D_d_point : 0);
if (!Hpose && !Hpoint) return Rot2::relativeBearing(d);
Matrix12 D_result_d;
Rot2 result = Rot2::relativeBearing(d, Hpose || Hpoint ? &D_result_d : 0);
@ -288,7 +288,7 @@ double Pose2::range(const Pose2& pose,
/* *************************************************************************
* New explanation, from scan.ml
* It finds the angle using a linear method:
* q = Pose2::transform_from(p) = t + R*p
* q = Pose2::transformFrom(p) = t + R*p
* We need to remove the centroids from the data to find the rotation
* using dp=[dpx;dpy] and q=[dqx;dqy] we have
* |dqx| |c -s| |dpx| |dpx -dpy| |c|

34
gtsam/geometry/Pose2.h
View File

@ -195,17 +195,17 @@ public:
/// @{
/** Return point coordinates in pose coordinate frame */
Point2 transform_to(const Point2& point,
OptionalJacobian<2, 3> H1 = boost::none,
OptionalJacobian<2, 2> H2 = boost::none) const;
Point2 transformTo(const Point2& point,
OptionalJacobian<2, 3> Dpose = boost::none,
OptionalJacobian<2, 2> Dpoint = boost::none) const;
/** Return point coordinates in global frame */
Point2 transform_from(const Point2& point,
OptionalJacobian<2, 3> H1 = boost::none,
OptionalJacobian<2, 2> H2 = boost::none) const;
Point2 transformFrom(const Point2& point,
OptionalJacobian<2, 3> Dpose = boost::none,
OptionalJacobian<2, 2> Dpoint = boost::none) const;
/** syntactic sugar for transform_from */
inline Point2 operator*(const Point2& point) const { return transform_from(point);}
/** syntactic sugar for transformFrom */
inline Point2 operator*(const Point2& point) const { return transformFrom(point);}
/// @}
/// @name Standard Interface
@ -289,6 +289,22 @@ public:
/// @}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// @name Deprecated
/// @{
Point2 transform_from(const Point2& point,
OptionalJacobian<2, 3> Dpose = boost::none,
OptionalJacobian<2, 2> Dpoint = boost::none) const {
return transformFrom(point, Dpose, Dpoint);
}
Point2 transform_to(const Point2& point,
OptionalJacobian<2, 3> Dpose = boost::none,
OptionalJacobian<2, 2> Dpoint = boost::none) const {
return transformTo(point, Dpose, Dpoint);
}
/// @}
#endif
private:
// Serialization function
@ -313,7 +329,7 @@ inline Matrix wedge<Pose2>(const Vector& xi) {
/**
* Calculate pose between a vector of 2D point correspondences (p,q)
* where q = Pose2::transform_from(p) = t + R*p
* where q = Pose2::transformFrom(p) = t + R*p
*/
typedef std::pair<Point2,Point2> Point2Pair;
GTSAM_EXPORT boost::optional<Pose2> align(const std::vector<Point2Pair>& pairs);

23
gtsam/geometry/Pose3.cpp
View File

@ -285,22 +285,31 @@ Matrix4 Pose3::matrix() const {
}
/* ************************************************************************* */
Pose3 Pose3::transformPoseFrom(const Pose3& aTb) const {
const Pose3& wTa = *this;
return wTa * aTb;
}
/* ************************************************************************* */
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
Pose3 Pose3::transform_to(const Pose3& pose) const {
Rot3 cRv = R_ * Rot3(pose.R_.inverse());
Point3 t = pose.transform_to(t_);
return Pose3(cRv, t);
}
#endif
/* ************************************************************************* */
Pose3 Pose3::transform_pose_to(const Pose3& pose, OptionalJacobian<6, 6> H1,
Pose3 Pose3::transformPoseTo(const Pose3& wTb, OptionalJacobian<6, 6> H1,
OptionalJacobian<6, 6> H2) const {
if (H1) *H1 = -pose.inverse().AdjointMap() * AdjointMap();
if (H1) *H1 = -wTb.inverse().AdjointMap() * AdjointMap();
if (H2) *H2 = I_6x6;
return inverse() * pose;
const Pose3& wTa = *this;
return wTa.inverse() * wTb;
}
/* ************************************************************************* */
Point3 Pose3::transform_from(const Point3& p, OptionalJacobian<3,6> Dpose,
Point3 Pose3::transformFrom(const Point3& p, OptionalJacobian<3,6> Dpose,
OptionalJacobian<3,3> Dpoint) const {
// Only get matrix once, to avoid multiple allocations,
// as well as multiple conversions in the Quaternion case
@ -316,7 +325,7 @@ Point3 Pose3::transform_from(const Point3& p, OptionalJacobian<3,6> Dpose,
}
/* ************************************************************************* */
Point3 Pose3::transform_to(const Point3& p, OptionalJacobian<3,6> Dpose,
Point3 Pose3::transformTo(const Point3& p, OptionalJacobian<3,6> Dpose,
OptionalJacobian<3,3> Dpoint) const {
// Only get transpose once, to avoid multiple allocations,
// as well as multiple conversions in the Quaternion case
@ -340,7 +349,7 @@ double Pose3::range(const Point3& point, OptionalJacobian<1, 6> H1,
OptionalJacobian<1, 3> H2) const {
Matrix36 D_local_pose;
Matrix3 D_local_point;
Point3 local = transform_to(point, H1 ? &D_local_pose : 0, H2 ? &D_local_point : 0);
Point3 local = transformTo(point, H1 ? &D_local_pose : 0, H2 ? &D_local_point : 0);
if (!H1 && !H2) {
return local.norm();
} else {
@ -366,7 +375,7 @@ Unit3 Pose3::bearing(const Point3& point, OptionalJacobian<2, 6> H1,
OptionalJacobian<2, 3> H2) const {
Matrix36 D_local_pose;
Matrix3 D_local_point;
Point3 local = transform_to(point, H1 ? &D_local_pose : 0, H2 ? &D_local_point : 0);
Point3 local = transformTo(point, H1 ? &D_local_pose : 0, H2 ? &D_local_point : 0);
if (!H1 && !H2) {
return Unit3(local);
} else {

49
gtsam/geometry/Pose3.h
View File

@ -81,7 +81,6 @@ public:
/**
* Create Pose3 by aligning two point pairs
* A pose aTb is estimated between pairs (a_point, b_point) such that a_point = aTb * b_point
* Meant to replace the deprecated function 'align', which orders the pairs the opposite way.
* Note this allows for noise on the points but in that case the mapping will not be exact.
*/
static boost::optional<Pose3> Align(const std::vector<Point3Pair>& abPointPairs);
@ -207,12 +206,12 @@ public:
* @param Dpoint optional 3*3 Jacobian wrpt point
* @return point in world coordinates
*/
Point3 transform_from(const Point3& p, OptionalJacobian<3, 6> Dpose =
Point3 transformFrom(const Point3& p, OptionalJacobian<3, 6> Dpose =
boost::none, OptionalJacobian<3, 3> Dpoint = boost::none) const;
/** syntactic sugar for transform_from */
/** syntactic sugar for transformFrom */
inline Point3 operator*(const Point3& p) const {
return transform_from(p);
return transformFrom(p);
}
/**
@ -222,7 +221,7 @@ public:
* @param Dpoint optional 3*3 Jacobian wrpt point
* @return point in Pose coordinates
*/
Point3 transform_to(const Point3& p, OptionalJacobian<3, 6> Dpose =
Point3 transformTo(const Point3& p, OptionalJacobian<3, 6> Dpose =
boost::none, OptionalJacobian<3, 3> Dpoint = boost::none) const;
/// @}
@ -253,13 +252,11 @@ public:
/** convert to 4*4 matrix */
Matrix4 matrix() const;
/** receives a pose in local coordinates and transforms it to world coordinates
* @deprecated: This is actually equivalent to transform_from, so it is WRONG! Use
* transform_pose_to instead. */
Pose3 transform_to(const Pose3& pose) const;
/** receives a pose in local coordinates and transforms it to world coordinates */
Pose3 transformPoseFrom(const Pose3& pose) const;
/** receives a pose in world coordinates and transforms it to local coordinates */
Pose3 transform_pose_to(const Pose3& pose, OptionalJacobian<6, 6> H1 = boost::none,
Pose3 transformPoseTo(const Pose3& pose, OptionalJacobian<6, 6> H1 = boost::none,
OptionalJacobian<6, 6> H2 = boost::none) const;
/**
@ -321,6 +318,30 @@ public:
GTSAM_EXPORT
friend std::ostream &operator<<(std::ostream &os, const Pose3& p);
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// @name Deprecated
/// @{
Point3 transform_from(const Point3& p,
OptionalJacobian<3, 6> Dpose = boost::none,
OptionalJacobian<3, 3> Dpoint = boost::none) const {
return transformFrom(p, Dpose, Dpoint);
}
Point3 transform_to(const Point3& p,
OptionalJacobian<3, 6> Dpose = boost::none,
OptionalJacobian<3, 3> Dpoint = boost::none) const {
return transformTo(p, Dpose, Dpoint);
}
Pose3 transform_pose_to(const Pose3& pose,
OptionalJacobian<6, 6> H1 = boost::none,
OptionalJacobian<6, 6> H2 = boost::none) const {
return transformPoseTo(pose, H1, H2);
}
/**
* @deprecated: this function is neither here not there. */
Pose3 transform_to(const Pose3& pose) const;
/// @}
#endif
private:
/** Serialization function */
friend class boost::serialization::access;
@ -351,12 +372,10 @@ inline Matrix wedge<Pose3>(const Vector& xi) {
return Pose3::wedge(xi(0), xi(1), xi(2), xi(3), xi(4), xi(5));
}
/**
* Calculate pose between a vector of 3D point correspondences (b_point, a_point)
* where a_point = Pose3::transform_from(b_point) = t + R*b_point
* @deprecated: use Pose3::Align with point pairs ordered the opposite way
*/
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
// deprecated: use Pose3::Align with point pairs ordered the opposite way
GTSAM_EXPORT boost::optional<Pose3> align(const std::vector<Point3Pair>& baPointPairs);
#endif
// For MATLAB wrapper
typedef std::vector<Pose3> Pose3Vector;

5
gtsam/geometry/SO3.cpp
View File

@ -31,7 +31,6 @@ namespace so3 {
void ExpmapFunctor::init(bool nearZeroApprox) {
nearZero = nearZeroApprox || (theta2 <= std::numeric_limits<double>::epsilon());
if (!nearZero) {
theta = std::sqrt(theta2); // rotation angle
sin_theta = std::sin(theta);
const double s2 = std::sin(theta / 2.0);
one_minus_cos = 2.0 * s2 * s2; // numerically better than [1 - cos(theta)]
@ -39,7 +38,7 @@ void ExpmapFunctor::init(bool nearZeroApprox) {
}
ExpmapFunctor::ExpmapFunctor(const Vector3& omega, bool nearZeroApprox)
: theta2(omega.dot(omega)) {
: theta2(omega.dot(omega)), theta(std::sqrt(theta2)) {
const double wx = omega.x(), wy = omega.y(), wz = omega.z();
W << 0.0, -wz, +wy, +wz, 0.0, -wx, -wy, +wx, 0.0;
init(nearZeroApprox);
@ -50,7 +49,7 @@ ExpmapFunctor::ExpmapFunctor(const Vector3& omega, bool nearZeroApprox)
}
ExpmapFunctor::ExpmapFunctor(const Vector3& axis, double angle, bool nearZeroApprox)
: theta2(angle * angle) {
: theta2(angle * angle), theta(angle) {
const double ax = axis.x(), ay = axis.y(), az = axis.z();
K << 0.0, -az, +ay, +az, 0.0, -ax, -ay, +ax, 0.0;
W = K * angle;

8
gtsam/geometry/StereoCamera.cpp
View File

@ -37,7 +37,7 @@ namespace gtsam {
StereoPoint2 StereoCamera::project2(const Point3& point,
OptionalJacobian<3,6> H1, OptionalJacobian<3,3> H2) const {
const Point3 q = leftCamPose_.transform_to(point);
const Point3 q = leftCamPose_.transformTo(point);
#ifdef GTSAM_THROW_CHEIRALITY_EXCEPTION
if (q.z() <= 0)
@ -94,7 +94,7 @@ namespace gtsam {
double Z = K_->baseline() * K_->fx() / (measured[0] - measured[1]);
double X = Z * (measured[0] - K_->px()) / K_->fx();
double Y = Z * (measured[2] - K_->py()) / K_->fy();
Point3 point = leftCamPose_.transform_from(Point3(X, Y, Z));
Point3 point = leftCamPose_.transformFrom(Point3(X, Y, Z));
return point;
}
@ -120,7 +120,7 @@ namespace gtsam {
-z_partial_uR, z_partial_uR, 0;
Matrix3 D_point_local;
const Point3 point = leftCamPose_.transform_from(local, H1, D_point_local);
const Point3 point = leftCamPose_.transformFrom(local, H1, D_point_local);
if(H2) {
*H2 = D_point_local * D_local_z;
@ -129,7 +129,7 @@ namespace gtsam {
return point;
}
return leftCamPose_.transform_from(local);
return leftCamPose_.transformFrom(local);
}
}

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

@ -134,7 +134,7 @@ TEST( CalibratedCamera, Dproject_point_pose)
Point2 result = camera.project(point1, Dpose, Dpoint);
Matrix numerical_pose = numericalDerivative21(project2, camera, point1);
Matrix numerical_point = numericalDerivative22(project2, camera, point1);
CHECK(assert_equal(Point3(-0.08, 0.08, 0.5), camera.pose().transform_to(point1)));
CHECK(assert_equal(Point3(-0.08, 0.08, 0.5), camera.pose().transformTo(point1)));
CHECK(assert_equal(Point2(-.16, .16), result));
CHECK(assert_equal(numerical_pose, Dpose, 1e-7));
CHECK(assert_equal(numerical_point, Dpoint, 1e-7));

6
gtsam/geometry/tests/testEssentialMatrix.cpp
View File

@ -115,9 +115,9 @@ TEST (EssentialMatrix, RoundTrip) {
//*************************************************************************
Point3 transform_to_(const EssentialMatrix& E, const Point3& point) {
return E.transform_to(point);
return E.transformTo(point);
}
TEST (EssentialMatrix, transform_to) {
TEST (EssentialMatrix, transformTo) {
// test with a more complicated EssentialMatrix
Rot3 aRb2 = Rot3::Yaw(M_PI / 3.0) * Rot3::Pitch(M_PI_4)
* Rot3::Roll(M_PI / 6.0);
@ -126,7 +126,7 @@ TEST (EssentialMatrix, transform_to) {
//EssentialMatrix E(aRb, Unit3(aTb).retract(Vector2(0.1, 0)));
static Point3 P(0.2, 0.7, -2);
Matrix actH1, actH2;
E.transform_to(P, actH1, actH2);
E.transformTo(P, actH1, actH2);
Matrix expH1 = numericalDerivative21(transform_to_, E, P), //
expH2 = numericalDerivative22(transform_to_, E, P);
EXPECT(assert_equal(expH1, actH1, 1e-8));

37
gtsam/geometry/tests/testPose2.cpp
View File

@ -266,45 +266,44 @@ TEST( Pose2, LogmapDerivative2) {
}
/* ************************************************************************* */
static Point2 transform_to_proxy(const Pose2& pose, const Point2& point) {
return pose.transform_to(point);
static Point2 transformTo_(const Pose2& pose, const Point2& point) {
return pose.transformTo(point);
}
TEST( Pose2, transform_to )
{
TEST(Pose2, transformTo) {
Pose2 pose(M_PI / 2.0, Point2(1, 2)); // robot at (1,2) looking towards y
Point2 point(-1, 4); // landmark at (-1,4)
// expected
Point2 expected(2, 2);
Matrix expectedH1 = (Matrix(2,3) << -1.0, 0.0, 2.0, 0.0, -1.0, -2.0).finished();
Matrix expectedH1 =
(Matrix(2, 3) << -1.0, 0.0, 2.0, 0.0, -1.0, -2.0).finished();
Matrix expectedH2 = (Matrix(2, 2) << 0.0, 1.0, -1.0, 0.0).finished();
// actual
Matrix actualH1, actualH2;
Point2 actual = pose.transform_to(point, actualH1, actualH2);
Point2 actual = pose.transformTo(point, actualH1, actualH2);
EXPECT(assert_equal(expected, actual));
EXPECT(assert_equal(expectedH1, actualH1));
Matrix numericalH1 = numericalDerivative21(transform_to_proxy, pose, point);
Matrix numericalH1 = numericalDerivative21(transformTo_, pose, point);
EXPECT(assert_equal(numericalH1, actualH1));
EXPECT(assert_equal(expectedH2, actualH2));
Matrix numericalH2 = numericalDerivative22(transform_to_proxy, pose, point);
Matrix numericalH2 = numericalDerivative22(transformTo_, pose, point);
EXPECT(assert_equal(numericalH2, actualH2));
}
/* ************************************************************************* */
static Point2 transform_from_proxy(const Pose2& pose, const Point2& point) {
return pose.transform_from(point);
static Point2 transformFrom_(const Pose2& pose, const Point2& point) {
return pose.transformFrom(point);
}
TEST (Pose2, transform_from)
{
TEST(Pose2, transformFrom) {
Pose2 pose(1., 0., M_PI / 2.0);
Point2 pt(2., 1.);
Matrix H1, H2;
Point2 actual = pose.transform_from(pt, H1, H2);
Point2 actual = pose.transformFrom(pt, H1, H2);
Point2 expected(0., 2.);
EXPECT(assert_equal(expected, actual));
@ -312,11 +311,11 @@ TEST (Pose2, transform_from)
Matrix H1_expected = (Matrix(2, 3) << 0., -1., -2., 1., 0., -1.).finished();
Matrix H2_expected = (Matrix(2, 2) << 0., -1., 1., 0.).finished();
Matrix numericalH1 = numericalDerivative21(transform_from_proxy, pose, pt);
Matrix numericalH1 = numericalDerivative21(transformFrom_, pose, pt);
EXPECT(assert_equal(H1_expected, H1));
EXPECT(assert_equal(H1_expected, numericalH1));
Matrix numericalH2 = numericalDerivative22(transform_from_proxy, pose, pt);
Matrix numericalH2 = numericalDerivative22(transformFrom_, pose, pt);
EXPECT(assert_equal(H2_expected, H2));
EXPECT(assert_equal(H2_expected, numericalH2));
}
@ -349,8 +348,8 @@ TEST(Pose2, compose_a)
Point2 point(sqrt(0.5), 3.0*sqrt(0.5));
Point2 expected_point(-1.0, -1.0);
Point2 actual_point1 = (pose1 * pose2).transform_to(point);
Point2 actual_point2 = pose2.transform_to(pose1.transform_to(point));
Point2 actual_point1 = (pose1 * pose2).transformTo(point);
Point2 actual_point2 = pose2.transformTo(pose1.transformTo(point));
EXPECT(assert_equal(expected_point, actual_point1));
EXPECT(assert_equal(expected_point, actual_point2));
}
@ -735,7 +734,7 @@ TEST(Pose2, align_2) {
vector<Point2Pair> correspondences;
Point2 p1(0,0), p2(10,0);
Point2 q1 = expected.transform_from(p1), q2 = expected.transform_from(p2);
Point2 q1 = expected.transformFrom(p1), q2 = expected.transformFrom(p2);
EXPECT(assert_equal(Point2(20,10),q1));
EXPECT(assert_equal(Point2(20,20),q2));
Point2Pair pq1(make_pair(p1, q1));
@ -750,7 +749,7 @@ namespace align_3 {
Point2 t(10,10);
Pose2 expected(Rot2::fromAngle(2*M_PI/3), t);
Point2 p1(0,0), p2(10,0), p3(10,10);
Point2 q1 = expected.transform_from(p1), q2 = expected.transform_from(p2), q3 = expected.transform_from(p3);
Point2 q1 = expected.transformFrom(p1), q2 = expected.transformFrom(p2), q3 = expected.transformFrom(p3);
}
TEST(Pose2, align_3) {

178
gtsam/geometry/tests/testPose3.cpp
View File

@ -316,80 +316,76 @@ TEST( Pose3, compose_inverse)
}
/* ************************************************************************* */
Point3 transform_from_(const Pose3& pose, const Point3& point) { return pose.transform_from(point); }
TEST( Pose3, Dtransform_from1_a)
{
Point3 transformFrom_(const Pose3& pose, const Point3& point) {
return pose.transformFrom(point);
}
TEST(Pose3, Dtransform_from1_a) {
Matrix actualDtransform_from1;
T.transform_from(P, actualDtransform_from1, boost::none);
Matrix numerical = numericalDerivative21(transform_from_,T,P);
T.transformFrom(P, actualDtransform_from1, boost::none);
Matrix numerical = numericalDerivative21(transformFrom_, T, P);
EXPECT(assert_equal(numerical, actualDtransform_from1, 1e-8));
}
TEST( Pose3, Dtransform_from1_b)
{
TEST(Pose3, Dtransform_from1_b) {
Pose3 origin;
Matrix actualDtransform_from1;
origin.transform_from(P, actualDtransform_from1, boost::none);
Matrix numerical = numericalDerivative21(transform_from_,origin,P);
origin.transformFrom(P, actualDtransform_from1, boost::none);
Matrix numerical = numericalDerivative21(transformFrom_, origin, P);
EXPECT(assert_equal(numerical, actualDtransform_from1, 1e-8));
}
TEST( Pose3, Dtransform_from1_c)
{
TEST(Pose3, Dtransform_from1_c) {
Point3 origin(0, 0, 0);
Pose3 T0(R, origin);
Matrix actualDtransform_from1;
T0.transform_from(P, actualDtransform_from1, boost::none);
Matrix numerical = numericalDerivative21(transform_from_,T0,P);
T0.transformFrom(P, actualDtransform_from1, boost::none);
Matrix numerical = numericalDerivative21(transformFrom_, T0, P);
EXPECT(assert_equal(numerical, actualDtransform_from1, 1e-8));
}
TEST( Pose3, Dtransform_from1_d)
{
TEST(Pose3, Dtransform_from1_d) {
Rot3 I;
Point3 t0(100, 0, 0);
Pose3 T0(I, t0);
Matrix actualDtransform_from1;
T0.transform_from(P, actualDtransform_from1, boost::none);
T0.transformFrom(P, actualDtransform_from1, boost::none);
// print(computed, "Dtransform_from1_d computed:");
Matrix numerical = numericalDerivative21(transform_from_,T0,P);
Matrix numerical = numericalDerivative21(transformFrom_, T0, P);
// print(numerical, "Dtransform_from1_d numerical:");
EXPECT(assert_equal(numerical, actualDtransform_from1, 1e-8));
}
/* ************************************************************************* */
TEST( Pose3, Dtransform_from2)
{
TEST(Pose3, Dtransform_from2) {
Matrix actualDtransform_from2;
T.transform_from(P, boost::none, actualDtransform_from2);
Matrix numerical = numericalDerivative22(transform_from_,T,P);
T.transformFrom(P, boost::none, actualDtransform_from2);
Matrix numerical = numericalDerivative22(transformFrom_, T, P);
EXPECT(assert_equal(numerical, actualDtransform_from2, 1e-8));
}
/* ************************************************************************* */
Point3 transform_to_(const Pose3& pose, const Point3& point) { return pose.transform_to(point); }
TEST( Pose3, Dtransform_to1)
{
Point3 transform_to_(const Pose3& pose, const Point3& point) {
return pose.transformTo(point);
}
TEST(Pose3, Dtransform_to1) {
Matrix computed;
T.transform_to(P, computed, boost::none);
T.transformTo(P, computed, boost::none);
Matrix numerical = numericalDerivative21(transform_to_, T, P);
EXPECT(assert_equal(numerical, computed, 1e-8));
}
/* ************************************************************************* */
TEST( Pose3, Dtransform_to2)
{
TEST(Pose3, Dtransform_to2) {
Matrix computed;
T.transform_to(P, boost::none, computed);
T.transformTo(P, boost::none, computed);
Matrix numerical = numericalDerivative22(transform_to_, T, P);
EXPECT(assert_equal(numerical, computed, 1e-8));
}
/* ************************************************************************* */
TEST( Pose3, transform_to_with_derivatives)
{
TEST(Pose3, transform_to_with_derivatives) {
Matrix actH1, actH2;
T.transform_to(P,actH1,actH2);
T.transformTo(P, actH1, actH2);
Matrix expH1 = numericalDerivative21(transform_to_, T, P),
expH2 = numericalDerivative22(transform_to_, T, P);
EXPECT(assert_equal(expH1, actH1, 1e-8));
@ -397,145 +393,87 @@ TEST( Pose3, transform_to_with_derivatives)
}
/* ************************************************************************* */
TEST( Pose3, transform_from_with_derivatives)
{
TEST(Pose3, transform_from_with_derivatives) {
Matrix actH1, actH2;
T.transform_from(P,actH1,actH2);
Matrix expH1 = numericalDerivative21(transform_from_, T,P),
expH2 = numericalDerivative22(transform_from_, T,P);
T.transformFrom(P, actH1, actH2);
Matrix expH1 = numericalDerivative21(transformFrom_, T, P),
expH2 = numericalDerivative22(transformFrom_, T, P);
EXPECT(assert_equal(expH1, actH1, 1e-8));
EXPECT(assert_equal(expH2, actH2, 1e-8));
}
/* ************************************************************************* */
TEST( Pose3, transform_to_translate)
{
Point3 actual = Pose3(Rot3(), Point3(1, 2, 3)).transform_to(Point3(10.,20.,30.));
TEST(Pose3, transform_to_translate) {
Point3 actual =
Pose3(Rot3(), Point3(1, 2, 3)).transformTo(Point3(10., 20., 30.));
Point3 expected(9., 18., 27.);
EXPECT(assert_equal(expected, actual));
}
/* ************************************************************************* */
TEST( Pose3, transform_to_rotate)
{
TEST(Pose3, transform_to_rotate) {
Pose3 transform(Rot3::Rodrigues(0, 0, -1.570796), Point3(0, 0, 0));
Point3 actual = transform.transform_to(Point3(2,1,10));
Point3 actual = transform.transformTo(Point3(2, 1, 10));
Point3 expected(-1, 2, 10);
EXPECT(assert_equal(expected, actual, 0.001));
}
/* ************************************************************************* */
TEST( Pose3, transform_to)
{
TEST(Pose3, transformTo) {
Pose3 transform(Rot3::Rodrigues(0, 0, -1.570796), Point3(2, 4, 0));
Point3 actual = transform.transform_to(Point3(3,2,10));
Point3 actual = transform.transformTo(Point3(3, 2, 10));
Point3 expected(2, 1, 10);
EXPECT(assert_equal(expected, actual, 0.001));
}
Pose3 transform_pose_to_(const Pose3& pose, const Pose3& pose2) { return pose.transform_pose_to(pose2); }
Pose3 transformPoseTo_(const Pose3& pose, const Pose3& pose2) {
return pose.transformPoseTo(pose2);
}
/* ************************************************************************* */
TEST( Pose3, transform_pose_to)
{
Pose3 origin = T.transform_pose_to(T);
TEST(Pose3, transformPoseTo) {
Pose3 origin = T.transformPoseTo(T);
EXPECT(assert_equal(Pose3{}, origin));
}
/* ************************************************************************* */
TEST( Pose3, transform_pose_to_with_derivatives)
{
TEST(Pose3, transformPoseTo_with_derivatives) {
Matrix actH1, actH2;
Pose3 res = T.transform_pose_to(T2,actH1,actH2);
Pose3 res = T.transformPoseTo(T2, actH1, actH2);
EXPECT(assert_equal(res, T.inverse().compose(T2)));
Matrix expH1 = numericalDerivative21(transform_pose_to_, T, T2),
expH2 = numericalDerivative22(transform_pose_to_, T, T2);
Matrix expH1 = numericalDerivative21(transformPoseTo_, T, T2),
expH2 = numericalDerivative22(transformPoseTo_, T, T2);
EXPECT(assert_equal(expH1, actH1, 1e-8));
EXPECT(assert_equal(expH2, actH2, 1e-8));
}
/* ************************************************************************* */
TEST( Pose3, transform_pose_to_with_derivatives2)
{
TEST(Pose3, transformPoseTo_with_derivatives2) {
Matrix actH1, actH2;
Pose3 res = T.transform_pose_to(T3,actH1,actH2);
Pose3 res = T.transformPoseTo(T3, actH1, actH2);
EXPECT(assert_equal(res, T.inverse().compose(T3)));
Matrix expH1 = numericalDerivative21(transform_pose_to_, T, T3),
expH2 = numericalDerivative22(transform_pose_to_, T, T3);
Matrix expH1 = numericalDerivative21(transformPoseTo_, T, T3),
expH2 = numericalDerivative22(transformPoseTo_, T, T3);
EXPECT(assert_equal(expH1, actH1, 1e-8));
EXPECT(assert_equal(expH2, actH2, 1e-8));
}
/* ************************************************************************* */
TEST( Pose3, transform_from)
{
Point3 actual = T3.transform_from(Point3(0,0,0));
TEST(Pose3, transformFrom) {
Point3 actual = T3.transformFrom(Point3(0, 0, 0));
Point3 expected = Point3(1., 2., 3.);
EXPECT(assert_equal(expected, actual));
}
/* ************************************************************************* */
TEST( Pose3, transform_roundtrip)
{
Point3 actual = T3.transform_from(T3.transform_to(Point3(12., -0.11,7.0)));
TEST(Pose3, transform_roundtrip) {
Point3 actual = T3.transformFrom(T3.transformTo(Point3(12., -0.11, 7.0)));
Point3 expected(12., -0.11, 7.0);
EXPECT(assert_equal(expected, actual));
}
/* ************************************************************************* */
TEST( Pose3, transformPose_to_origin)
{
// transform to origin
Pose3 actual = T3.transform_to(Pose3());
EXPECT(assert_equal(T3, actual, 1e-8));
}
/* ************************************************************************* */
TEST( Pose3, transformPose_to_itself)
{
// transform to itself
Pose3 actual = T3.transform_to(T3);
EXPECT(assert_equal(Pose3(), actual, 1e-8));
}
/* ************************************************************************* */
TEST( Pose3, transformPose_to_translation)
{
// transform translation only
Rot3 r = Rot3::Rodrigues(-1.570796,0,0);
Pose3 pose2(r, Point3(21.,32.,13.));
Pose3 actual = pose2.transform_to(Pose3(Rot3(), Point3(1,2,3)));
Pose3 expected(r, Point3(20.,30.,10.));
EXPECT(assert_equal(expected, actual, 1e-8));
}
/* ************************************************************************* */
TEST( Pose3, transformPose_to_simple_rotate)
{
// transform translation only
Rot3 r = Rot3::Rodrigues(0,0,-1.570796);
Pose3 pose2(r, Point3(21.,32.,13.));
Pose3 transform(r, Point3(1,2,3));
Pose3 actual = pose2.transform_to(transform);
Pose3 expected(Rot3(), Point3(-30.,20.,10.));
EXPECT(assert_equal(expected, actual, 0.001));
}
/* ************************************************************************* */
TEST( Pose3, transformPose_to)
{
// transform to
Rot3 r = Rot3::Rodrigues(0,0,-1.570796); //-90 degree yaw
Rot3 r2 = Rot3::Rodrigues(0,0,0.698131701); //40 degree yaw
Pose3 pose2(r2, Point3(21.,32.,13.));
Pose3 transform(r, Point3(1,2,3));
Pose3 actual = pose2.transform_to(transform);
Pose3 expected(Rot3::Rodrigues(0,0,2.26892803), Point3(-30.,20.,10.));
EXPECT(assert_equal(expected, actual, 0.001));
}
/* ************************************************************************* */
TEST(Pose3, Retract_LocalCoordinates)
{
@ -789,9 +727,9 @@ TEST(Pose3, Align2) {
vector<Point3Pair> correspondences;
Point3 p1(0,0,1), p2(10,0,2), p3(20,-10,30);
Point3 q1 = expected.transform_from(p1),
q2 = expected.transform_from(p2),
q3 = expected.transform_from(p3);
Point3 q1 = expected.transformFrom(p1),
q2 = expected.transformFrom(p2),
q3 = expected.transformFrom(p3);
Point3Pair ab1(make_pair(q1, p1));
Point3Pair ab2(make_pair(q2, p2));
Point3Pair ab3(make_pair(q3, p3));

14
gtsam/geometry/tests/testRot3.cpp
View File

@ -103,6 +103,20 @@ Rot3 slow_but_correct_Rodrigues(const Vector& w) {
return Rot3(R);
}
/* ************************************************************************* */
TEST( Rot3, AxisAngle)
{
Vector axis = Vector3(0., 1., 0.); // rotation around Y
double angle = 3.14 / 4.0;
Rot3 expected(0.707388, 0, 0.706825,
0, 1, 0,
-0.706825, 0, 0.707388);
Rot3 actual = Rot3::AxisAngle(axis, angle);
CHECK(assert_equal(expected,actual,1e-5));
Rot3 actual2 = Rot3::AxisAngle(axis, angle-2*M_PI);
CHECK(assert_equal(expected,actual2,1e-5));
}
/* ************************************************************************* */
TEST( Rot3, Rodrigues)
{

28
gtsam/geometry/tests/testSO3.cpp
View File

@ -82,6 +82,34 @@ TEST(SO3, ChartDerivatives) {
CHECK_CHART_DERIVATIVES(R2, R1);
}
/* ************************************************************************* */
TEST(SO3, Expmap) {
Vector axis = Vector3(0., 1., 0.); // rotation around Y
double angle = 3.14 / 4.0;
Matrix expected(3,3);
expected << 0.707388, 0, 0.706825, 0, 1, 0, -0.706825, 0, 0.707388;
// axis angle version
so3::ExpmapFunctor f1(axis, angle);
SO3 actual1 = f1.expmap();
CHECK(assert_equal(expected, actual1.matrix(), 1e-5));
// axis angle version, negative angle
so3::ExpmapFunctor f2(axis, angle - 2*M_PI);
SO3 actual2 = f2.expmap();
CHECK(assert_equal(expected, actual2.matrix(), 1e-5));
// omega version
so3::ExpmapFunctor f3(axis * angle);
SO3 actual3 = f3.expmap();
CHECK(assert_equal(expected, actual3.matrix(), 1e-5));
// omega version, negative angle
so3::ExpmapFunctor f4(axis * (angle - 2*M_PI));
SO3 actual4 = f4.expmap();
CHECK(assert_equal(expected, actual4.matrix(), 1e-5));
}
/* ************************************************************************* */
namespace exmap_derivative {
static const Vector3 w(0.1, 0.27, -0.2);

6
gtsam/geometry/triangulation.h
View File

@ -256,7 +256,7 @@ Point3 triangulatePoint3(const std::vector<Pose3>& poses,
#ifdef GTSAM_THROW_CHEIRALITY_EXCEPTION
// verify that the triangulated point lies in front of all cameras
for(const Pose3& pose: poses) {
const Point3& p_local = pose.transform_to(point);
const Point3& p_local = pose.transformTo(point);
if (p_local.z() <= 0)
throw(TriangulationCheiralityException());
}
@ -304,7 +304,7 @@ Point3 triangulatePoint3(
#ifdef GTSAM_THROW_CHEIRALITY_EXCEPTION
// verify that the triangulated point lies in front of all cameras
for(const CAMERA& camera: cameras) {
const Point3& p_local = camera.pose().transform_to(point);
const Point3& p_local = camera.pose().transformTo(point);
if (p_local.z() <= 0)
throw(TriangulationCheiralityException());
}
@ -484,7 +484,7 @@ TriangulationResult triangulateSafe(const CameraSet<CAMERA>& cameras,
#ifdef GTSAM_THROW_CHEIRALITY_EXCEPTION
// verify that the triangulated point lies in front of all cameras
// Only needed if this was not yet handled by exception
const Point3& p_local = pose.transform_to(point);
const Point3& p_local = pose.transformTo(point);
if (p_local.z() <= 0)
return TriangulationResult::BehindCamera();
#endif

7
gtsam/inference/EliminationTree-inst.h
View File

@ -49,8 +49,7 @@ namespace gtsam {
// Do dense elimination step
KeyVector keyAsVector(1); keyAsVector[0] = key;
std::pair<boost::shared_ptr<ConditionalType>, boost::shared_ptr<FactorType> > eliminationResult =
function(gatheredFactors, Ordering(keyAsVector));
auto eliminationResult = function(gatheredFactors, Ordering(keyAsVector));
// Add conditional to BayesNet
output->push_back(eliminationResult.first);
@ -190,13 +189,13 @@ namespace gtsam {
{
gttic(EliminationTree_eliminate);
// Allocate result
boost::shared_ptr<BayesNetType> result = boost::make_shared<BayesNetType>();
auto result = boost::make_shared<BayesNetType>();
// Run tree elimination algorithm
FastVector<sharedFactor> remainingFactors = inference::EliminateTree(result, *this, function);
// Add remaining factors that were not involved with eliminated variables
boost::shared_ptr<FactorGraphType> allRemainingFactors = boost::make_shared<FactorGraphType>();
auto allRemainingFactors = boost::make_shared<FactorGraphType>();
allRemainingFactors->push_back(remainingFactors_.begin(), remainingFactors_.end());
allRemainingFactors->push_back(remainingFactors.begin(), remainingFactors.end());

3
gtsam/inference/Factor.h
View File

@ -28,6 +28,9 @@
#include <gtsam/inference/Key.h>
namespace gtsam {
/// Define collection types:
typedef FastVector<FactorIndex> FactorIndices;
typedef FastSet<FactorIndex> FactorIndexSet;
/**
* This is the base class for all factor types. It is templated on a KEY type,

2
gtsam/inference/VariableIndex-inl.h
View File

@ -25,7 +25,7 @@ namespace gtsam {
/* ************************************************************************* */
template<class FG>
void VariableIndex::augment(const FG& factors,
boost::optional<const FastVector<size_t>&> newFactorIndices) {
boost::optional<const FactorIndices&> newFactorIndices) {
gttic(VariableIndex_augment);
// Augment index for each factor

8
gtsam/inference/VariableIndex.cpp
View File

@ -35,8 +35,8 @@ void VariableIndex::print(const string& str, const KeyFormatter& keyFormatter) c
cout << "nEntries = " << nEntries() << ", nFactors = " << nFactors() << "\n";
for(KeyMap::value_type key_factors: index_) {
cout << "var " << keyFormatter(key_factors.first) << ":";
for(const size_t factor: key_factors.second)
cout << " " << factor;
for(const auto index: key_factors.second)
cout << " " << index;
cout << "\n";
}
cout.flush();
@ -48,8 +48,8 @@ void VariableIndex::outputMetisFormat(ostream& os) const {
// run over variables, which will be hyper-edges.
for(KeyMap::value_type key_factors: index_) {
// every variable is a hyper-edge covering its factors
for(const size_t factor: key_factors.second)
os << (factor+1) << " "; // base 1
for(const auto index: key_factors.second)
os << (index+1) << " "; // base 1
os << "\n";
}
os << flush;

30
gtsam/inference/VariableIndex.h
View File

@ -17,6 +17,7 @@
#pragma once
#include <gtsam/inference/Factor.h>
#include <gtsam/inference/Key.h>
#include <gtsam/base/FastMap.h>
#include <gtsam/base/FastVector.h>
@ -43,7 +44,7 @@ class GTSAM_EXPORT VariableIndex {
public:
typedef boost::shared_ptr<VariableIndex> shared_ptr;
typedef FastVector<size_t> Factors;
typedef FactorIndices Factors;
typedef Factors::iterator Factor_iterator;
typedef Factors::const_iterator Factor_const_iterator;
@ -63,7 +64,7 @@ public:
/// @name Standard Constructors
/// @{
/** Default constructor, creates an empty VariableIndex */
/// Default constructor, creates an empty VariableIndex
VariableIndex() : nFactors_(0), nEntries_(0) {}
/**
@ -77,19 +78,16 @@ public:
/// @name Standard Interface
/// @{
/**
* The number of variable entries. This is one greater than the variable
* with the highest index.
*/
/// The number of variable entries. This is equal to the number of unique variable Keys.
size_t size() const { return index_.size(); }
/** The number of factors in the original factor graph */
/// The number of factors in the original factor graph
size_t nFactors() const { return nFactors_; }
/** The number of nonzero blocks, i.e. the number of variable-factor entries */
/// The number of nonzero blocks, i.e. the number of variable-factor entries
size_t nEntries() const { return nEntries_; }
/** Access a list of factors by variable */
/// Access a list of factors by variable
const Factors& operator[](Key variable) const {
KeyMap::const_iterator item = index_.find(variable);
if(item == index_.end())
@ -102,10 +100,10 @@ public:
/// @name Testable
/// @{
/** Test for equality (for unit tests and debug assertions). */
/// Test for equality (for unit tests and debug assertions).
bool equals(const VariableIndex& other, double tol=0.0) const;
/** Print the variable index (for unit tests and debugging). */
/// Print the variable index (for unit tests and debugging).
void print(const std::string& str = "VariableIndex: ",
const KeyFormatter& keyFormatter = DefaultKeyFormatter) const;
@ -125,7 +123,7 @@ public:
* solving problems incrementally.
*/
template<class FG>
void augment(const FG& factors, boost::optional<const FastVector<size_t>&> newFactorIndices = boost::none);
void augment(const FG& factors, boost::optional<const FactorIndices&> newFactorIndices = boost::none);
/**
* Remove entries corresponding to the specified factors. NOTE: We intentionally do not decrement
@ -140,17 +138,17 @@ public:
template<typename ITERATOR, class FG>
void remove(ITERATOR firstFactor, ITERATOR lastFactor, const FG& factors);
/** Remove unused empty variables (in debug mode verifies they are empty). */
/// Remove unused empty variables (in debug mode verifies they are empty).
template<typename ITERATOR>
void removeUnusedVariables(ITERATOR firstKey, ITERATOR lastKey);
/** Iterator to the first variable entry */
/// Iterator to the first variable entry
const_iterator begin() const { return index_.begin(); }
/** Iterator to the first variable entry */
/// Iterator to the first variable entry
const_iterator end() const { return index_.end(); }
/** Find the iterator for the requested variable entry */
/// Find the iterator for the requested variable entry
const_iterator find(Key key) const { return index_.find(key); }
protected:

25
gtsam/linear/GaussianBayesNet.cpp
View File

@ -138,9 +138,9 @@ namespace gtsam {
//}
/* ************************************************************************* */
pair<Matrix, Vector> GaussianBayesNet::matrix() const {
Ordering GaussianBayesNet::ordering() const {
GaussianFactorGraph factorGraph(*this);
KeySet keys = factorGraph.keys();
auto keys = factorGraph.keys();
// add frontal keys in order
Ordering ordering;
for (const sharedConditional& cg : *this)
@ -151,10 +151,21 @@ namespace gtsam {
}
}
// add remaining keys in case Bayes net is incomplete
for (Key key: keys)
ordering.push_back(key);
// return matrix and RHS
for (Key key : keys) ordering.push_back(key);
return ordering;
}
/* ************************************************************************* */
pair<Matrix, Vector> GaussianBayesNet::matrix(boost::optional<const Ordering&> ordering) const {
if (ordering) {
// Convert to a GaussianFactorGraph and use its machinery
GaussianFactorGraph factorGraph(*this);
return factorGraph.jacobian(ordering);
} else {
// recursively call with default ordering
const auto defaultOrdering = this->ordering();
return matrix(defaultOrdering);
}
}
///* ************************************************************************* */
@ -181,11 +192,11 @@ namespace gtsam {
double logDet = 0.0;
for(const sharedConditional& cg: *this) {
if(cg->get_model()) {
Vector diag = cg->get_R().diagonal();
Vector diag = cg->R().diagonal();
cg->get_model()->whitenInPlace(diag);
logDet += diag.unaryExpr(ptr_fun<double,double>(log)).sum();
} else {
logDet += cg->get_R().diagonal().unaryExpr(ptr_fun<double,double>(log)).sum();
logDet += cg->R().diagonal().unaryExpr(ptr_fun<double,double>(log)).sum();
}
}
return logDet;

12
gtsam/linear/GaussianBayesNet.h
View File

@ -74,6 +74,14 @@ namespace gtsam {
/// Version of optimize for incomplete BayesNet, needs solution for missing variables
VectorValues optimize(const VectorValues& solutionForMissing) const;
/**
* Return ordering corresponding to a topological sort.
* There are many topological sorts of a Bayes net. This one
* corresponds to the one that makes 'matrix' below upper-triangular.
* In case Bayes net is incomplete any non-frontal are added to the end.
*/
Ordering ordering() const;
///@}
///@name Linear Algebra
@ -81,8 +89,10 @@ namespace gtsam {
/**
* Return (dense) upper-triangular matrix representation
* Will return upper-triangular matrix only when using 'ordering' above.
* In case Bayes net is incomplete zero columns are added to the end.
*/
std::pair<Matrix, Vector> matrix() const;
std::pair<Matrix, Vector> matrix(boost::optional<const Ordering&> ordering = boost::none) const;
/**
* Optimize along the gradient direction, with a closed-form computation to perform the line

2
gtsam/linear/GaussianBayesTree-inl.h
View File

@ -43,7 +43,7 @@ double logDeterminant(const typename BAYESTREE::sharedClique& clique) {
double result = 0.0;
// this clique
result += clique->conditional()->get_R().diagonal().unaryExpr(std::ptr_fun<double,double>(log)).sum();
result += clique->conditional()->R().diagonal().unaryExpr(std::ptr_fun<double,double>(log)).sum();
// sum of children
for(const typename BAYESTREE::sharedClique& child: clique->children_)

2
gtsam/linear/GaussianBayesTree.cpp
View File

@ -37,7 +37,7 @@ namespace gtsam {
/* ************************************************************************* */
double logDeterminant(const GaussianBayesTreeClique::shared_ptr& clique, double& parentSum)
{
parentSum += clique->conditional()->get_R().diagonal().unaryExpr(std::ptr_fun<double,double>(log)).sum();
parentSum += clique->conditional()->R().diagonal().unaryExpr(std::ptr_fun<double,double>(log)).sum();
assert(false);
return 0;
}

22
gtsam/linear/GaussianConditional.cpp
View File

@ -62,7 +62,7 @@ namespace gtsam {
cout << (boost::format("[%1%]")%(formatter(*it))).str() << " ";
}
cout << endl;
cout << formatMatrixIndented(" R = ", get_R()) << endl;
cout << formatMatrixIndented(" R = ", R()) << endl;
for (const_iterator it = beginParents() ; it != endParents() ; ++it) {
cout << formatMatrixIndented((boost::format(" S[%1%] = ")%(formatter(*it))).str(), getA(it))
<< endl;
@ -84,8 +84,8 @@ namespace gtsam {
// check if R_ and d_ are linear independent
for (DenseIndex i = 0; i < Ab_.rows(); i++) {
list<Vector> rows1, rows2;
rows1.push_back(Vector(get_R().row(i)));
rows2.push_back(Vector(c->get_R().row(i)));
rows1.push_back(Vector(R().row(i)));
rows2.push_back(Vector(c->R().row(i)));
// check if the matrices are the same
// iterate over the parents_ map
@ -120,10 +120,10 @@ namespace gtsam {
const Vector xS = x.vector(KeyVector(beginParents(), endParents()));
// Update right-hand-side
const Vector rhs = get_d() - get_S() * xS;
const Vector rhs = d() - S() * xS;
// Solve matrix
const Vector solution = get_R().triangularView<Eigen::Upper>().solve(rhs);
const Vector solution = R().triangularView<Eigen::Upper>().solve(rhs);
// Check for indeterminant solution
if (solution.hasNaN()) {
@ -134,7 +134,7 @@ namespace gtsam {
VectorValues result;
DenseIndex vectorPosition = 0;
for (const_iterator frontal = beginFrontals(); frontal != endFrontals(); ++frontal) {
result.insert(*frontal, solution.segment(vectorPosition, getDim(frontal)));
result.emplace(*frontal, solution.segment(vectorPosition, getDim(frontal)));
vectorPosition += getDim(frontal);
}
@ -149,10 +149,10 @@ namespace gtsam {
// Instead of updating getb(), update the right-hand-side from the given rhs
const Vector rhsR = rhs.vector(KeyVector(beginFrontals(), endFrontals()));
xS = rhsR - get_S() * xS;
xS = rhsR - S() * xS;
// Solve Matrix
Vector soln = get_R().triangularView<Eigen::Upper>().solve(xS);
Vector soln = R().triangularView<Eigen::Upper>().solve(xS);
// Scale by sigmas
if (model_)
@ -162,7 +162,7 @@ namespace gtsam {
VectorValues result;
DenseIndex vectorPosition = 0;
for (const_iterator frontal = beginFrontals(); frontal != endFrontals(); ++frontal) {
result.insert(*frontal, soln.segment(vectorPosition, getDim(frontal)));
result.emplace(*frontal, soln.segment(vectorPosition, getDim(frontal)));
vectorPosition += getDim(frontal);
}
@ -172,13 +172,13 @@ namespace gtsam {
/* ************************************************************************* */
void GaussianConditional::solveTransposeInPlace(VectorValues& gy) const {
Vector frontalVec = gy.vector(KeyVector(beginFrontals(), endFrontals()));
frontalVec = gtsam::backSubstituteUpper(frontalVec, Matrix(get_R()));
frontalVec = R().transpose().triangularView<Eigen::Lower>().solve(frontalVec);
// Check for indeterminant solution
if (frontalVec.hasNaN()) throw IndeterminantLinearSystemException(this->keys().front());
for (const_iterator it = beginParents(); it!= endParents(); it++)
gy[*it] += -1.0 * Matrix(getA(it)).transpose() * frontalVec;
gy[*it].noalias() += -1.0 * getA(it).transpose() * frontalVec;
// Scale by sigmas
if (model_)

19
gtsam/linear/GaussianConditional.h
View File

@ -94,16 +94,16 @@ namespace gtsam {
bool equals(const GaussianFactor&cg, double tol = 1e-9) const;
/** Return a view of the upper-triangular R block of the conditional */
constABlock get_R() const { return Ab_.range(0, nrFrontals()); }
constABlock R() const { return Ab_.range(0, nrFrontals()); }
/** Get a view of the parent blocks. */
constABlock get_S() const { return Ab_.range(nrFrontals(), size()); }
constABlock S() const { return Ab_.range(nrFrontals(), size()); }
/** Get a view of the S matrix for the variable pointed to by the given key iterator */
constABlock get_S(const_iterator variable) const { return BaseFactor::getA(variable); }
constABlock S(const_iterator it) const { return BaseFactor::getA(it); }
/** Get a view of the r.h.s. vector d */
const constBVector get_d() const { return BaseFactor::getb(); }
const constBVector d() const { return BaseFactor::getb(); }
/**
* Solves a conditional Gaussian and writes the solution into the entries of
@ -130,8 +130,17 @@ namespace gtsam {
void scaleFrontalsBySigma(VectorValues& gy) const;
// __declspec(deprecated) void scaleFrontalsBySigma(VectorValues& gy) const; // FIXME: depreciated flag doesn't appear to exist?
private:
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// @name Deprecated
/// @{
constABlock get_R() const { return R(); }
constABlock get_S() const { return S(); }
constABlock get_S(const_iterator it) const { return S(it); }
const constBVector get_d() const { return d(); }
/// @}
#endif
private:
/** Serialization function */
friend class boost::serialization::access;
template<class Archive>

4
gtsam/linear/GaussianDensity.cpp
View File

@ -35,8 +35,8 @@ namespace gtsam {
for(const_iterator it = beginFrontals(); it != endFrontals(); ++it)
cout << (boost::format("[%1%]")%(formatter(*it))).str() << " ";
cout << endl;
gtsam::print(Matrix(get_R()), "R: ");
gtsam::print(Vector(get_d()), "d: ");
gtsam::print(Matrix(R()), "R: ");
gtsam::print(Vector(d()), "d: ");
if(model_)
model_->print("Noise model: ");
}

4
gtsam/linear/GaussianFactorGraph.cpp
View File

@ -61,7 +61,7 @@ namespace gtsam {
for (GaussianFactor::const_iterator it = gf->begin(); it != gf->end(); it++) {
map<Key,size_t>::iterator it2 = spec.find(*it);
if ( it2 == spec.end() ) {
spec.insert(make_pair(*it, gf->getDim(it)));
spec.emplace(*it, gf->getDim(it));
}
}
}
@ -246,7 +246,7 @@ namespace gtsam {
if (blocks.count(j))
blocks[j] += Bj;
else
blocks.insert(make_pair(j,Bj));
blocks.emplace(j,Bj);
}
}
return blocks;

11
gtsam/linear/HessianFactor.cpp
View File

@ -305,7 +305,7 @@ Matrix HessianFactor::information() const {
VectorValues HessianFactor::hessianDiagonal() const {
VectorValues d;
for (DenseIndex j = 0; j < (DenseIndex)size(); ++j) {
d.insert(keys_[j], info_.diagonal(j));
d.emplace(keys_[j], info_.diagonal(j));
}
return d;
}
@ -436,7 +436,7 @@ VectorValues HessianFactor::gradientAtZero() const {
VectorValues g;
size_t n = size();
for (size_t j = 0; j < n; ++j)
g.insert(keys_[j], -info_.aboveDiagonalBlock(j, n));
g.emplace(keys_[j], -info_.aboveDiagonalBlock(j, n));
return g;
}
@ -487,6 +487,11 @@ boost::shared_ptr<GaussianConditional> HessianFactor::eliminateCholesky(const Or
// Erase the eliminated keys in this factor
keys_.erase(begin(), begin() + nFrontals);
} catch (const CholeskyFailed&) {
#ifndef NDEBUG
cout << "Partial Cholesky on HessianFactor failed." << endl;
keys.print("Frontal keys ");
print("HessianFactor:");
#endif
throw IndeterminantLinearSystemException(keys.front());
}
@ -513,7 +518,7 @@ VectorValues HessianFactor::solve() {
std::size_t offset = 0;
for (DenseIndex j = 0; j < (DenseIndex)n; ++j) {
const DenseIndex dim = info_.getDim(j);
delta.insert(keys_[j], solution.segment(offset, dim));
delta.emplace(keys_[j], solution.segment(offset, dim));
offset += dim;
}

10
gtsam/linear/IterativeSolver.cpp
View File

@ -117,7 +117,7 @@ void KeyInfo::initialize(const GaussianFactorGraph &fg) {
for (size_t i = 0; i < n; ++i) {
const size_t key = ordering_[i];
const size_t dim = colspec.find(key)->second;
insert(make_pair(key, KeyInfoEntry(i, dim, start)));
this->emplace(key, KeyInfoEntry(i, dim, start));
start += dim;
}
numCols_ = start;
@ -126,8 +126,8 @@ void KeyInfo::initialize(const GaussianFactorGraph &fg) {
/****************************************************************************/
vector<size_t> KeyInfo::colSpec() const {
std::vector<size_t> result(size(), 0);
for ( const KeyInfo::value_type &item: *this ) {
result[item.second.index()] = item.second.dim();
for ( const auto &item: *this ) {
result[item.second.index] = item.second.dim;
}
return result;
}
@ -135,8 +135,8 @@ vector<size_t> KeyInfo::colSpec() const {
/****************************************************************************/
VectorValues KeyInfo::x0() const {
VectorValues result;
for ( const KeyInfo::value_type &item: *this ) {
result.insert(item.first, Vector::Zero(item.second.dim()));
for ( const auto &item: *this ) {
result.emplace(item.first, Vector::Zero(item.second.dim));
}
return result;
}

23
gtsam/linear/IterativeSolver.h
View File

@ -32,8 +32,8 @@
namespace gtsam {
// Forward declarations
struct KeyInfoEntry;
class KeyInfo;
class KeyInfoEntry;
class GaussianFactorGraph;
class Values;
class VectorValues;
@ -109,27 +109,14 @@ public:
/**
* Handy data structure for iterative solvers
* key to (index, dimension, colstart)
* key to (index, dimension, start)
*/
class GTSAM_EXPORT KeyInfoEntry: public boost::tuple<Key, size_t, Key> {
public:
typedef boost::tuple<Key, size_t, Key> Base;
struct GTSAM_EXPORT KeyInfoEntry {
size_t index, dim, start;
KeyInfoEntry() {
}
KeyInfoEntry(size_t idx, size_t d, Key start) :
Base(idx, d, start) {
}
size_t index() const {
return this->get<0>();
}
size_t dim() const {
return this->get<1>();
}
size_t colstart() const {
return this->get<2>();
index(idx), dim(d), start(start) {
}
};

75
gtsam/linear/JacobianFactor.cpp
View File

@ -102,10 +102,9 @@ JacobianFactor::JacobianFactor(const Key i1, const Matrix& A1, Key i2,
}
/* ************************************************************************* */
JacobianFactor::JacobianFactor(const HessianFactor& factor) :
Base(factor), Ab_(
VerticalBlockMatrix::LikeActiveViewOf(factor.info(),
factor.rows())) {
JacobianFactor::JacobianFactor(const HessianFactor& factor)
: Base(factor),
Ab_(VerticalBlockMatrix::LikeActiveViewOf(factor.info(), factor.rows())) {
// Copy Hessian into our matrix and then do in-place Cholesky
Ab_.full() = factor.info().selfadjointView();
@ -114,16 +113,19 @@ JacobianFactor::JacobianFactor(const HessianFactor& factor) :
bool success;
boost::tie(maxrank, success) = choleskyCareful(Ab_.matrix());
// Check for indefinite system
if (!success)
throw IndeterminantLinearSystemException(factor.keys().front());
// Check that Cholesky succeeded OR it managed to factor the full Hessian.
// THe latter case occurs with non-positive definite matrices arising from QP.
if (success || maxrank == factor.rows() - 1) {
// Zero out lower triangle
Ab_.matrix().topRows(maxrank).triangularView<Eigen::StrictlyLower>() =
Matrix::Zero(maxrank, Ab_.matrix().cols());
// FIXME: replace with triangular system
Ab_.rowEnd() = maxrank;
model_ = SharedDiagonal(); // should be same as Unit::Create(maxrank);
model_ = SharedDiagonal(); // is equivalent to Unit::Create(maxrank)
} else {
// indefinite system
throw IndeterminantLinearSystemException(factor.keys().front());
}
}
/* ************************************************************************* */
@ -164,9 +166,10 @@ boost::tuple<FastVector<DenseIndex>, DenseIndex, DenseIndex> _countDims(
n += vardim;
} else {
if(!(varDims[jointVarpos] == vardim)) {
cout << "Factor " << sourceFactorI << " variable " << DefaultKeyFormatter(sourceFactor.keys()[sourceVarpos]) <<
" has different dimensionality of " << vardim << " instead of " << varDims[jointVarpos] << endl;
exit(1);
std::stringstream ss;
ss << "Factor " << sourceFactorI << " variable " << DefaultKeyFormatter(sourceFactor.keys()[sourceVarpos]) <<
" has different dimensionality of " << vardim << " instead of " << varDims[jointVarpos];
throw std::runtime_error(ss.str());
}
}
#else
@ -429,10 +432,9 @@ Vector JacobianFactor::unweighted_error(const VectorValues& c) const {
/* ************************************************************************* */
Vector JacobianFactor::error_vector(const VectorValues& c) const {
if (model_)
return model_->whiten(unweighted_error(c));
else
return unweighted_error(c);
Vector e = unweighted_error(c);
if (model_) model_->whitenInPlace(e);
return e;
}
/* ************************************************************************* */
@ -473,10 +475,10 @@ VectorValues JacobianFactor::hessianDiagonal() const {
for (size_t k = 0; k < nj; ++k) {
Vector column_k = Ab_(pos).col(k);
if (model_)
column_k = model_->whiten(column_k);
model_->whitenInPlace(column_k);
dj(k) = dot(column_k, column_k);
}
d.insert(j, dj);
d.emplace(j, dj);
}
return d;
}
@ -495,7 +497,7 @@ map<Key, Matrix> JacobianFactor::hessianBlockDiagonal() const {
Matrix Aj = Ab_(pos);
if (model_)
Aj = model_->Whiten(Aj);
blocks.insert(make_pair(j, Aj.transpose() * Aj));
blocks.emplace(j, Aj.transpose() * Aj);
}
return blocks;
}
@ -540,29 +542,38 @@ void JacobianFactor::updateHessian(const KeyVector& infoKeys,
/* ************************************************************************* */
Vector JacobianFactor::operator*(const VectorValues& x) const {
Vector Ax = Vector::Zero(Ab_.rows());
Vector Ax(Ab_.rows());
Ax.setZero();
if (empty())
return Ax;
// Just iterate over all A matrices and multiply in correct config part
for (size_t pos = 0; pos < size(); ++pos)
Ax += Ab_(pos) * x[keys_[pos]];
for (size_t pos = 0; pos < size(); ++pos) {
// http://eigen.tuxfamily.org/dox/TopicWritingEfficientProductExpression.html
Ax.noalias() += Ab_(pos) * x[keys_[pos]];
}
return model_ ? model_->whiten(Ax) : Ax;
if (model_) model_->whitenInPlace(Ax);
return Ax;
}
/* ************************************************************************* */
void JacobianFactor::transposeMultiplyAdd(double alpha, const Vector& e,
VectorValues& x) const {
Vector E = alpha * (model_ ? model_->whiten(e) : e);
Vector E(e.size());
E.noalias() = alpha * e;
if (model_) model_->whitenInPlace(E);
// Just iterate over all A matrices and insert Ai^e into VectorValues
for (size_t pos = 0; pos < size(); ++pos) {
Key j = keys_[pos];
// Create the value as a zero vector if it does not exist.
pair<VectorValues::iterator, bool> xi = x.tryInsert(j, Vector());
if (xi.second)
xi.first->second = Vector::Zero(getDim(begin() + pos));
xi.first->second += Ab_(pos).transpose()*E;
const Key j = keys_[pos];
// To avoid another malloc if key exists, we explicitly check
auto it = x.find(j);
if (it != x.end()) {
// http://eigen.tuxfamily.org/dox/TopicWritingEfficientProductExpression.html
it->second.noalias() += Ab_(pos).transpose() * E;
} else {
x.emplace(j, Ab_(pos).transpose() * E);
}
}
}
@ -624,8 +635,8 @@ VectorValues JacobianFactor::gradientAtZero() const {
Vector b = getb();
// Gradient is really -A'*b / sigma^2
// transposeMultiplyAdd will divide by sigma once, so we need one more
Vector b_sigma = model_ ? model_->whiten(b) : b;
this->transposeMultiplyAdd(-1.0, b_sigma, g); // g -= A'*b/sigma^2
if (model_) model_->whitenInPlace(b);
this->transposeMultiplyAdd(-1.0, b, g); // g -= A'*b/sigma^2
return g;
}

9
gtsam/linear/PCGSolver.cpp
View File

@ -89,7 +89,7 @@ void GaussianFactorGraphSystem::multiply(const Vector &x, Vector& AtAx) const {
VectorValues vvX = buildVectorValues(x, keyInfo_);
// VectorValues form of A'Ax for multiplyHessianAdd
VectorValues vvAtAx;
VectorValues vvAtAx = keyInfo_.x0(); // crucial for performance
// vvAtAx += 1.0 * A'Ax for each factor
gfg_.multiplyHessianAdd(1.0, vvX, vvAtAx);
@ -132,14 +132,14 @@ VectorValues buildVectorValues(const Vector &v, const Ordering &ordering,
DenseIndex offset = 0;
for (size_t i = 0; i < ordering.size(); ++i) {
const Key &key = ordering[i];
const Key key = ordering[i];
map<Key, size_t>::const_iterator it = dimensions.find(key);
if (it == dimensions.end()) {
throw invalid_argument(
"buildVectorValues: inconsistent ordering and dimensions");
}
const size_t dim = it->second;
result.insert(key, v.segment(offset, dim));
result.emplace(key, v.segment(offset, dim));
offset += dim;
}
@ -150,8 +150,7 @@ VectorValues buildVectorValues(const Vector &v, const Ordering &ordering,
VectorValues buildVectorValues(const Vector &v, const KeyInfo &keyInfo) {
VectorValues result;
for ( const KeyInfo::value_type &item: keyInfo ) {
result.insert(item.first,
v.segment(item.second.colstart(), item.second.dim()));
result.emplace(item.first, v.segment(item.second.start, item.second.dim));
}
return result;
}

28
gtsam/linear/Preconditioner.h
View File

@ -70,21 +70,20 @@ public:
Preconditioner() {}
virtual ~Preconditioner() {}
/* Computation Interfaces */
/*
* Abstract interface for raw vectors. VectorValues is a speed bottleneck
* and Yong-Dian has profiled preconditioners (outside GTSAM) with the the
* three methods below. In GTSAM, unfortunately, we are still using the
* VectorValues methods called in iterative-inl.h
*/
/* implement x = L^{-1} y */
/// implement x = L^{-1} y
virtual void solve(const Vector& y, Vector &x) const = 0;
// virtual void solve(const VectorValues& y, VectorValues &x) const = 0;
/* implement x = L^{-T} y */
/// implement x = L^{-T} y
virtual void transposeSolve(const Vector& y, Vector& x) const = 0;
// virtual void transposeSolve(const VectorValues& y, VectorValues &x) const = 0;
// /* implement x = L^{-1} L^{-T} y */
// virtual void fullSolve(const Vector& y, Vector &x) const = 0;
// virtual void fullSolve(const VectorValues& y, VectorValues &x) const = 0;
/* build/factorize the preconditioner */
/// build/factorize the preconditioner
virtual void build(
const GaussianFactorGraph &gfg,
const KeyInfo &info,
@ -113,14 +112,7 @@ public:
/* Computation Interfaces for raw vector */
virtual void solve(const Vector& y, Vector &x) const { x = y; }
// virtual void solve(const VectorValues& y, VectorValues& x) const { x = y; }
virtual void transposeSolve(const Vector& y, Vector& x) const { x = y; }
// virtual void transposeSolve(const VectorValues& y, VectorValues& x) const { x = y; }
// virtual void fullSolve(const Vector& y, Vector &x) const { x = y; }
// virtual void fullSolve(const VectorValues& y, VectorValues& x) const { x = y; }
virtual void build(
const GaussianFactorGraph &gfg,
const KeyInfo &info,
@ -145,8 +137,6 @@ public:
/* Computation Interfaces for raw vector */
virtual void solve(const Vector& y, Vector &x) const;
virtual void transposeSolve(const Vector& y, Vector& x) const ;
// virtual void fullSolve(const Vector& y, Vector &x) const ;
virtual void build(
const GaussianFactorGraph &gfg,
const KeyInfo &info,

545
gtsam/linear/SubgraphBuilder.cpp Normal file
View File

@ -0,0 +1,545 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2019, 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 SubgraphBuilder.cpp
* @date Dec 31, 2009
* @author Frank Dellaert, Yong-Dian Jian
*/
#include <gtsam/base/DSFVector.h>
#include <gtsam/base/FastMap.h>
#include <gtsam/inference/Ordering.h>
#include <gtsam/inference/VariableIndex.h>
#include <gtsam/linear/Errors.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/SubgraphBuilder.h>
#include <boost/algorithm/string.hpp>
#include <boost/archive/text_iarchive.hpp>
#include <boost/archive/text_oarchive.hpp>
#include <boost/serialization/vector.hpp>
#include <algorithm>
#include <cmath>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <numeric> // accumulate
#include <queue>
#include <set>
#include <stdexcept>
#include <vector>
using std::cout;
using std::endl;
using std::ostream;
using std::vector;
namespace gtsam {
/*****************************************************************************/
/* sort the container and return permutation index with default comparator */
template <typename Container>
static vector<size_t> sort_idx(const Container &src) {
typedef typename Container::value_type T;
const size_t n = src.size();
vector<std::pair<size_t, T> > tmp;
tmp.reserve(n);
for (size_t i = 0; i < n; i++) tmp.emplace_back(i, src[i]);
/* sort */
std::stable_sort(tmp.begin(), tmp.end());
/* copy back */
vector<size_t> idx;
idx.reserve(n);
for (size_t i = 0; i < n; i++) {
idx.push_back(tmp[i].first);
}
return idx;
}
/*****************************************************************************/
static vector<size_t> iidSampler(const vector<double> &weight, const size_t n) {
/* compute the sum of the weights */
const double sum = std::accumulate(weight.begin(), weight.end(), 0.0);
if (sum == 0.0) {
throw std::runtime_error("Weight vector has no non-zero weights");
}
/* make a normalized and accumulated version of the weight vector */
const size_t m = weight.size();
vector<double> cdf;
cdf.reserve(m);
for (size_t i = 0; i < m; ++i) {
cdf.push_back(weight[i] / sum);
}
vector<double> acc(m);
std::partial_sum(cdf.begin(), cdf.end(), acc.begin());
/* iid sample n times */
vector<size_t> samples;
samples.reserve(n);
const double denominator = (double)RAND_MAX;
for (size_t i = 0; i < n; ++i) {
const double value = rand() / denominator;
/* binary search the interval containing "value" */
const auto it = std::lower_bound(acc.begin(), acc.end(), value);
const size_t index = it - acc.begin();
samples.push_back(index);
}
return samples;
}
/*****************************************************************************/
static vector<size_t> UniqueSampler(const vector<double> &weight,
const size_t n) {
const size_t m = weight.size();
if (n > m) throw std::invalid_argument("UniqueSampler: invalid input size");
vector<size_t> samples;
size_t count = 0;
vector<bool> touched(m, false);
while (count < n) {
vector<size_t> localIndices;
localIndices.reserve(n - count);
vector<double> localWeights;
localWeights.reserve(n - count);
/* collect data */
for (size_t i = 0; i < m; ++i) {
if (!touched[i]) {
localIndices.push_back(i);
localWeights.push_back(weight[i]);
}
}
/* sampling and cache results */
vector<size_t> samples = iidSampler(localWeights, n - count);
for (const size_t &index : samples) {
if (touched[index] == false) {
touched[index] = true;
samples.push_back(index);
if (++count >= n) break;
}
}
}
return samples;
}
/****************************************************************************/
Subgraph::Subgraph(const vector<size_t> &indices) {
edges_.reserve(indices.size());
for (const size_t &index : indices) {
const Edge e {index,1.0};
edges_.push_back(e);
}
}
/****************************************************************************/
vector<size_t> Subgraph::edgeIndices() const {
vector<size_t> eid;
eid.reserve(size());
for (const Edge &edge : edges_) {
eid.push_back(edge.index);
}
return eid;
}
/****************************************************************************/
void Subgraph::save(const std::string &fn) const {
std::ofstream os(fn.c_str());
boost::archive::text_oarchive oa(os);
oa << *this;
os.close();
}
/****************************************************************************/
Subgraph Subgraph::load(const std::string &fn) {
std::ifstream is(fn.c_str());
boost::archive::text_iarchive ia(is);
Subgraph subgraph;
ia >> subgraph;
is.close();
return subgraph;
}
/****************************************************************************/
ostream &operator<<(ostream &os, const Subgraph::Edge &edge) {
if (edge.weight != 1.0)
os << edge.index << "(" << std::setprecision(2) << edge.weight << ")";
else
os << edge.index;
return os;
}
/****************************************************************************/
ostream &operator<<(ostream &os, const Subgraph &subgraph) {
os << "Subgraph" << endl;
for (const auto &e : subgraph.edges()) {
os << e << ", ";
}
return os;
}
/*****************************************************************************/
void SubgraphBuilderParameters::print() const { print(cout); }
/***************************************************************************************/
void SubgraphBuilderParameters::print(ostream &os) const {
os << "SubgraphBuilderParameters" << endl
<< "skeleton: " << skeletonTranslator(skeletonType) << endl
<< "skeleton weight: " << skeletonWeightTranslator(skeletonWeight)
<< endl
<< "augmentation weight: "
<< augmentationWeightTranslator(augmentationWeight) << endl;
}
/*****************************************************************************/
ostream &operator<<(ostream &os, const SubgraphBuilderParameters &p) {
p.print(os);
return os;
}
/*****************************************************************************/
SubgraphBuilderParameters::Skeleton
SubgraphBuilderParameters::skeletonTranslator(const std::string &src) {
std::string s = src;
boost::algorithm::to_upper(s);
if (s == "NATURALCHAIN")
return NATURALCHAIN;
else if (s == "BFS")
return BFS;
else if (s == "KRUSKAL")
return KRUSKAL;
throw std::invalid_argument(
"SubgraphBuilderParameters::skeletonTranslator undefined string " + s);
return KRUSKAL;
}
/****************************************************************/
std::string SubgraphBuilderParameters::skeletonTranslator(Skeleton s) {
if (s == NATURALCHAIN)
return "NATURALCHAIN";
else if (s == BFS)
return "BFS";
else if (s == KRUSKAL)
return "KRUSKAL";
else
return "UNKNOWN";
}
/****************************************************************/
SubgraphBuilderParameters::SkeletonWeight
SubgraphBuilderParameters::skeletonWeightTranslator(const std::string &src) {
std::string s = src;
boost::algorithm::to_upper(s);
if (s == "EQUAL")
return EQUAL;
else if (s == "RHS")
return RHS_2NORM;
else if (s == "LHS")
return LHS_FNORM;
else if (s == "RANDOM")
return RANDOM;
throw std::invalid_argument(
"SubgraphBuilderParameters::skeletonWeightTranslator undefined string " +
s);
return EQUAL;
}
/****************************************************************/
std::string SubgraphBuilderParameters::skeletonWeightTranslator(
SkeletonWeight w) {
if (w == EQUAL)
return "EQUAL";
else if (w == RHS_2NORM)
return "RHS";
else if (w == LHS_FNORM)
return "LHS";
else if (w == RANDOM)
return "RANDOM";
else
return "UNKNOWN";
}
/****************************************************************/
SubgraphBuilderParameters::AugmentationWeight
SubgraphBuilderParameters::augmentationWeightTranslator(
const std::string &src) {
std::string s = src;
boost::algorithm::to_upper(s);
if (s == "SKELETON") return SKELETON;
// else if (s == "STRETCH") return STRETCH;
// else if (s == "GENERALIZED_STRETCH") return GENERALIZED_STRETCH;
throw std::invalid_argument(
"SubgraphBuilder::Parameters::augmentationWeightTranslator undefined "
"string " +
s);
return SKELETON;
}
/****************************************************************/
std::string SubgraphBuilderParameters::augmentationWeightTranslator(
AugmentationWeight w) {
if (w == SKELETON) return "SKELETON";
// else if ( w == STRETCH ) return "STRETCH";
// else if ( w == GENERALIZED_STRETCH ) return "GENERALIZED_STRETCH";
else
return "UNKNOWN";
}
/****************************************************************/
vector<size_t> SubgraphBuilder::buildTree(const GaussianFactorGraph &gfg,
const FastMap<Key, size_t> &ordering,
const vector<double> &weights) const {
const SubgraphBuilderParameters &p = parameters_;
switch (p.skeletonType) {
case SubgraphBuilderParameters::NATURALCHAIN:
return natural_chain(gfg);
break;
case SubgraphBuilderParameters::BFS:
return bfs(gfg);
break;
case SubgraphBuilderParameters::KRUSKAL:
return kruskal(gfg, ordering, weights);
break;
default:
std::cerr << "SubgraphBuilder::buildTree undefined skeleton type" << endl;
break;
}
return vector<size_t>();
}
/****************************************************************/
vector<size_t> SubgraphBuilder::unary(const GaussianFactorGraph &gfg) const {
vector<size_t> unaryFactorIndices;
size_t index = 0;
for (const auto &factor : gfg) {
if (factor->size() == 1) {
unaryFactorIndices.push_back(index);
}
index++;
}
return unaryFactorIndices;
}
/****************************************************************/
vector<size_t> SubgraphBuilder::natural_chain(
const GaussianFactorGraph &gfg) const {
vector<size_t> chainFactorIndices;
size_t index = 0;
for (const GaussianFactor::shared_ptr &gf : gfg) {
if (gf->size() == 2) {
const Key k0 = gf->keys()[0], k1 = gf->keys()[1];
if ((k1 - k0) == 1 || (k0 - k1) == 1) chainFactorIndices.push_back(index);
}
index++;
}
return chainFactorIndices;
}
/****************************************************************/
vector<size_t> SubgraphBuilder::bfs(const GaussianFactorGraph &gfg) const {
const VariableIndex variableIndex(gfg);
/* start from the first key of the first factor */
Key seed = gfg[0]->keys()[0];
const size_t n = variableIndex.size();
/* each vertex has self as the predecessor */
vector<size_t> bfsFactorIndices;
bfsFactorIndices.reserve(n - 1);
/* Initialize */
std::queue<size_t> q;
q.push(seed);
std::set<size_t> flags;
flags.insert(seed);
/* traversal */
while (!q.empty()) {
const size_t head = q.front();
q.pop();
for (const size_t index : variableIndex[head]) {
const GaussianFactor &gf = *gfg[index];
for (const size_t key : gf.keys()) {
if (flags.count(key) == 0) {
q.push(key);
flags.insert(key);
bfsFactorIndices.push_back(index);
}
}
}
}
return bfsFactorIndices;
}
/****************************************************************/
vector<size_t> SubgraphBuilder::kruskal(const GaussianFactorGraph &gfg,
const FastMap<Key, size_t> &ordering,
const vector<double> &weights) const {
const VariableIndex variableIndex(gfg);
const size_t n = variableIndex.size();
const vector<size_t> sortedIndices = sort_idx(weights);
/* initialize buffer */
vector<size_t> treeIndices;
treeIndices.reserve(n - 1);
// container for acsendingly sorted edges
DSFVector dsf(n);
size_t count = 0;
double sum = 0.0;
for (const size_t index : sortedIndices) {
const GaussianFactor &gf = *gfg[index];
const auto keys = gf.keys();
if (keys.size() != 2) continue;
const size_t u = ordering.find(keys[0])->second,
v = ordering.find(keys[1])->second;
if (dsf.find(u) != dsf.find(v)) {
dsf.merge(u, v);
treeIndices.push_back(index);
sum += weights[index];
if (++count == n - 1) break;
}
}
return treeIndices;
}
/****************************************************************/
vector<size_t> SubgraphBuilder::sample(const vector<double> &weights,
const size_t t) const {
return UniqueSampler(weights, t);
}
/****************************************************************/
Subgraph SubgraphBuilder::operator()(
const GaussianFactorGraph &gfg) const {
const auto &p = parameters_;
const auto inverse_ordering = Ordering::Natural(gfg);
const FastMap<Key, size_t> forward_ordering = inverse_ordering.invert();
const size_t n = inverse_ordering.size(), m = gfg.size();
// Make sure the subgraph preconditioner does not include more than half of
// the edges beyond the spanning tree, or we might as well solve the whole
// thing.
size_t numExtraEdges = n * p.augmentationFactor;
const size_t numRemaining = m - (n - 1);
numExtraEdges = std::min(numExtraEdges, numRemaining / 2);
// Calculate weights
vector<double> weights = this->weights(gfg);
// Build spanning tree.
const vector<size_t> tree = buildTree(gfg, forward_ordering, weights);
if (tree.size() != n - 1) {
throw std::runtime_error(
"SubgraphBuilder::operator() failure: tree.size() != n-1");
}
// Downweight the tree edges to zero.
for (const size_t index : tree) {
weights[index] = 0.0;
}
/* decide how many edges to augment */
vector<size_t> offTree = sample(weights, numExtraEdges);
vector<size_t> subgraph = unary(gfg);
subgraph.insert(subgraph.end(), tree.begin(), tree.end());
subgraph.insert(subgraph.end(), offTree.begin(), offTree.end());
return Subgraph(subgraph);
}
/****************************************************************/
SubgraphBuilder::Weights SubgraphBuilder::weights(
const GaussianFactorGraph &gfg) const {
const size_t m = gfg.size();
Weights weight;
weight.reserve(m);
for (const GaussianFactor::shared_ptr &gf : gfg) {
switch (parameters_.skeletonWeight) {
case SubgraphBuilderParameters::EQUAL:
weight.push_back(1.0);
break;
case SubgraphBuilderParameters::RHS_2NORM: {
if (JacobianFactor::shared_ptr jf =
boost::dynamic_pointer_cast<JacobianFactor>(gf)) {
weight.push_back(jf->getb().norm());
} else if (HessianFactor::shared_ptr hf =
boost::dynamic_pointer_cast<HessianFactor>(gf)) {
weight.push_back(hf->linearTerm().norm());
}
} break;
case SubgraphBuilderParameters::LHS_FNORM: {
if (JacobianFactor::shared_ptr jf =
boost::dynamic_pointer_cast<JacobianFactor>(gf)) {
weight.push_back(std::sqrt(jf->getA().squaredNorm()));
} else if (HessianFactor::shared_ptr hf =
boost::dynamic_pointer_cast<HessianFactor>(gf)) {
weight.push_back(std::sqrt(hf->information().squaredNorm()));
}
} break;
case SubgraphBuilderParameters::RANDOM:
weight.push_back(std::rand() % 100 + 1.0);
break;
default:
throw std::invalid_argument(
"SubgraphBuilder::weights: undefined weight scheme ");
break;
}
}
return weight;
}
/*****************************************************************************/
GaussianFactorGraph::shared_ptr buildFactorSubgraph(
const GaussianFactorGraph &gfg, const Subgraph &subgraph,
const bool clone) {
auto subgraphFactors = boost::make_shared<GaussianFactorGraph>();
subgraphFactors->reserve(subgraph.size());
for (const auto &e : subgraph) {
const auto factor = gfg[e.index];
subgraphFactors->push_back(clone ? factor->clone(): factor);
}
return subgraphFactors;
}
/**************************************************************************************************/
std::pair<GaussianFactorGraph::shared_ptr, GaussianFactorGraph::shared_ptr> //
splitFactorGraph(const GaussianFactorGraph &factorGraph, const Subgraph &subgraph) {
// Get the subgraph by calling cheaper method
auto subgraphFactors = buildFactorSubgraph(factorGraph, subgraph, false);
// Now, copy all factors then set subGraph factors to zero
auto remaining = boost::make_shared<GaussianFactorGraph>(factorGraph);
for (const auto &e : subgraph) {
remaining->remove(e.index);
}
return std::make_pair(subgraphFactors, remaining);
}
/*****************************************************************************/
} // namespace gtsam

182
gtsam/linear/SubgraphBuilder.h Normal file
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@ -0,0 +1,182 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2019, 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 SubgraphBuilder.h
* @date Dec 31, 2009
* @author Frank Dellaert, Yong-Dian Jian
*/
#pragma once
#include <gtsam/base/FastMap.h>
#include <gtsam/base/types.h>
#include <gtsam/dllexport.h>
#include <boost/serialization/nvp.hpp>
#include <boost/shared_ptr.hpp>
#include <vector>
namespace boost {
namespace serialization {
class access;
} /* namespace serialization */
} /* namespace boost */
namespace gtsam {
// Forward declarations
class GaussianFactorGraph;
struct PreconditionerParameters;
/**************************************************************************/
class GTSAM_EXPORT Subgraph {
public:
struct GTSAM_EXPORT Edge {
size_t index; /* edge id */
double weight; /* edge weight */
inline bool isUnitWeight() const { return (weight == 1.0); }
friend std::ostream &operator<<(std::ostream &os, const Edge &edge);
private:
friend class boost::serialization::access;
template <class Archive>
void serialize(Archive &ar, const unsigned int /*version*/) {
ar &BOOST_SERIALIZATION_NVP(index);
ar &BOOST_SERIALIZATION_NVP(weight);
}
};
typedef std::vector<Edge> Edges;
typedef std::vector<size_t> EdgeIndices;
typedef Edges::iterator iterator;
typedef Edges::const_iterator const_iterator;
protected:
Edges edges_; /* index to the factors */
public:
Subgraph() {}
Subgraph(const Subgraph &subgraph) : edges_(subgraph.edges()) {}
Subgraph(const Edges &edges) : edges_(edges) {}
Subgraph(const std::vector<size_t> &indices);
inline const Edges &edges() const { return edges_; }
inline size_t size() const { return edges_.size(); }
EdgeIndices edgeIndices() const;
iterator begin() { return edges_.begin(); }
const_iterator begin() const { return edges_.begin(); }
iterator end() { return edges_.end(); }
const_iterator end() const { return edges_.end(); }
void save(const std::string &fn) const;
static Subgraph load(const std::string &fn);
friend std::ostream &operator<<(std::ostream &os, const Subgraph &subgraph);
private:
friend class boost::serialization::access;
template <class Archive>
void serialize(Archive &ar, const unsigned int /*version*/) {
ar &BOOST_SERIALIZATION_NVP(edges_);
}
};
/****************************************************************************/
struct GTSAM_EXPORT SubgraphBuilderParameters {
typedef boost::shared_ptr<SubgraphBuilderParameters> shared_ptr;
enum Skeleton {
/* augmented tree */
NATURALCHAIN = 0, /* natural ordering of the graph */
BFS, /* breadth-first search tree */
KRUSKAL, /* maximum weighted spanning tree */
} skeletonType;
enum SkeletonWeight { /* how to weigh the graph edges */
EQUAL = 0, /* every block edge has equal weight */
RHS_2NORM, /* use the 2-norm of the rhs */
LHS_FNORM, /* use the frobenius norm of the lhs */
RANDOM, /* bounded random edge weight */
} skeletonWeight;
enum AugmentationWeight { /* how to weigh the graph edges */
SKELETON = 0, /* use the same weights in building
the skeleton */
// STRETCH, /* stretch in the
// laplacian sense */ GENERALIZED_STRETCH /*
// the generalized stretch defined in
// jian2013iros */
} augmentationWeight;
/// factor multiplied with n, yields number of extra edges.
double augmentationFactor;
SubgraphBuilderParameters()
: skeletonType(KRUSKAL),
skeletonWeight(RANDOM),
augmentationWeight(SKELETON),
augmentationFactor(1.0) {}
virtual ~SubgraphBuilderParameters() {}
/* for serialization */
void print() const;
virtual void print(std::ostream &os) const;
friend std::ostream &operator<<(std::ostream &os,
const PreconditionerParameters &p);
static Skeleton skeletonTranslator(const std::string &s);
static std::string skeletonTranslator(Skeleton s);
static SkeletonWeight skeletonWeightTranslator(const std::string &s);
static std::string skeletonWeightTranslator(SkeletonWeight w);
static AugmentationWeight augmentationWeightTranslator(const std::string &s);
static std::string augmentationWeightTranslator(AugmentationWeight w);
};
/*****************************************************************************/
class GTSAM_EXPORT SubgraphBuilder {
public:
typedef SubgraphBuilder Base;
typedef std::vector<double> Weights;
SubgraphBuilder(
const SubgraphBuilderParameters &p = SubgraphBuilderParameters())
: parameters_(p) {}
virtual ~SubgraphBuilder() {}
virtual Subgraph operator()(const GaussianFactorGraph &jfg) const;
private:
std::vector<size_t> buildTree(const GaussianFactorGraph &gfg,
const FastMap<Key, size_t> &ordering,
const std::vector<double> &weights) const;
std::vector<size_t> unary(const GaussianFactorGraph &gfg) const;
std::vector<size_t> natural_chain(const GaussianFactorGraph &gfg) const;
std::vector<size_t> bfs(const GaussianFactorGraph &gfg) const;
std::vector<size_t> kruskal(const GaussianFactorGraph &gfg,
const FastMap<Key, size_t> &ordering,
const std::vector<double> &weights) const;
std::vector<size_t> sample(const std::vector<double> &weights,
const size_t t) const;
Weights weights(const GaussianFactorGraph &gfg) const;
SubgraphBuilderParameters parameters_;
};
/** Select the factors in a factor graph according to the subgraph. */
boost::shared_ptr<GaussianFactorGraph> buildFactorSubgraph(
const GaussianFactorGraph &gfg, const Subgraph &subgraph, const bool clone);
/** Split the graph into a subgraph and the remaining edges.
* Note that the remaining factorgraph has null factors. */
std::pair<boost::shared_ptr<GaussianFactorGraph>, boost::shared_ptr<GaussianFactorGraph> >
splitFactorGraph(const GaussianFactorGraph &factorGraph, const Subgraph &subgraph);
} // namespace gtsam

599
gtsam/linear/SubgraphPreconditioner.cpp
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@ -1,6 +1,6 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
@ -12,451 +12,85 @@
/**
* @file SubgraphPreconditioner.cpp
* @date Dec 31, 2009
* @author: Frank Dellaert
* @author Frank Dellaert, Yong-Dian Jian
*/
#include <gtsam/linear/SubgraphPreconditioner.h>
#include <gtsam/linear/GaussianConditional.h>
#include <gtsam/linear/HessianFactor.h>
#include <gtsam/linear/SubgraphBuilder.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/JacobianFactor.h>
#include <gtsam/linear/GaussianEliminationTree.h>
#include <gtsam/inference/Ordering.h>
#include <gtsam/inference/VariableIndex.h>
#include <gtsam/base/DSFVector.h>
#include <gtsam/base/FastMap.h>
#include <gtsam/base/FastVector.h>
#include <gtsam/base/types.h>
#include <gtsam/base/Vector.h>
#include <boost/algorithm/string.hpp>
#include <boost/archive/text_iarchive.hpp>
#include <boost/archive/text_oarchive.hpp>
#include <boost/serialization/vector.hpp>
#include <boost/range/adaptor/reversed.hpp>
#include <boost/shared_ptr.hpp>
#include <algorithm>
#include <cmath>
#include <cstdlib>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <iterator>
#include <list>
#include <map>
#include <numeric> // accumulate
#include <queue>
#include <set>
#include <stdexcept>
#include <string>
#include <utility>
#include <vector>
using namespace std;
using std::cout;
using std::endl;
using std::vector;
using std::ostream;
namespace gtsam {
/* ************************************************************************* */
static GaussianFactorGraph::shared_ptr convertToJacobianFactors(const GaussianFactorGraph &gfg) {
GaussianFactorGraph::shared_ptr result(new GaussianFactorGraph());
for(const GaussianFactor::shared_ptr &gf: gfg) {
JacobianFactor::shared_ptr jf = boost::dynamic_pointer_cast<JacobianFactor>(gf);
static Vector getSubvector(const Vector &src, const KeyInfo &keyInfo,
const KeyVector &keys) {
/* a cache of starting index and dim */
vector<std::pair<size_t, size_t> > cache;
cache.reserve(3);
/* figure out dimension by traversing the keys */
size_t dim = 0;
for (const Key &key : keys) {
const KeyInfoEntry &entry = keyInfo.find(key)->second;
cache.emplace_back(entry.start, entry.dim);
dim += entry.dim;
}
/* use the cache to fill the result */
Vector result(dim);
size_t index = 0;
for (const auto &p : cache) {
result.segment(index, p.second) = src.segment(p.first, p.second);
index += p.second;
}
return result;
}
/*****************************************************************************/
static void setSubvector(const Vector &src, const KeyInfo &keyInfo,
const KeyVector &keys, Vector &dst) {
size_t index = 0;
for (const Key &key : keys) {
const KeyInfoEntry &entry = keyInfo.find(key)->second;
const size_t keyDim = entry.dim;
dst.segment(entry.start, keyDim) = src.segment(index, keyDim);
index += keyDim;
}
}
/* ************************************************************************* */
// Convert any non-Jacobian factors to Jacobians (e.g. Hessian -> Jacobian with
// Cholesky)
static GaussianFactorGraph::shared_ptr convertToJacobianFactors(
const GaussianFactorGraph &gfg) {
auto result = boost::make_shared<GaussianFactorGraph>();
for (const auto &factor : gfg)
if (factor) {
auto jf = boost::dynamic_pointer_cast<JacobianFactor>(factor);
if (!jf) {
jf = boost::make_shared<JacobianFactor>(*gf); // Convert any non-Jacobian factors to Jacobians (e.g. Hessian -> Jacobian with Cholesky)
jf = boost::make_shared<JacobianFactor>(*factor);
}
result->push_back(jf);
}
return result;
}
/*****************************************************************************/
static std::vector<size_t> iidSampler(const vector<double> &weight, const size_t n) {
/* compute the sum of the weights */
const double sum = std::accumulate(weight.begin(), weight.end(), 0.0);
/* make a normalized and accumulated version of the weight vector */
const size_t m = weight.size();
vector<double> w; w.reserve(m);
for ( size_t i = 0 ; i < m ; ++i ) {
w.push_back(weight[i]/sum);
}
vector<double> acc(m);
std::partial_sum(w.begin(),w.end(),acc.begin());
/* iid sample n times */
vector<size_t> result; result.reserve(n);
const double denominator = (double)RAND_MAX;
for ( size_t i = 0 ; i < n ; ++i ) {
const double value = rand() / denominator;
/* binary search the interval containing "value" */
vector<double>::iterator it = std::lower_bound(acc.begin(), acc.end(), value);
size_t idx = it - acc.begin();
result.push_back(idx);
}
return result;
}
/*****************************************************************************/
vector<size_t> uniqueSampler(const vector<double> &weight, const size_t n) {
const size_t m = weight.size();
if ( n > m ) throw std::invalid_argument("uniqueSampler: invalid input size");
vector<size_t> result;
size_t count = 0;
std::vector<bool> touched(m, false);
while ( count < n ) {
std::vector<size_t> localIndices; localIndices.reserve(n-count);
std::vector<double> localWeights; localWeights.reserve(n-count);
/* collect data */
for ( size_t i = 0 ; i < m ; ++i ) {
if ( !touched[i] ) {
localIndices.push_back(i);
localWeights.push_back(weight[i]);
}
}
/* sampling and cache results */
vector<size_t> samples = iidSampler(localWeights, n-count);
for ( const size_t &id: samples ) {
if ( touched[id] == false ) {
touched[id] = true ;
result.push_back(id);
if ( ++count >= n ) break;
}
}
}
return result;
}
/****************************************************************************/
Subgraph::Subgraph(const std::vector<size_t> &indices) {
edges_.reserve(indices.size());
for ( const size_t &idx: indices ) {
edges_.push_back(SubgraphEdge(idx, 1.0));
}
}
/****************************************************************************/
std::vector<size_t> Subgraph::edgeIndices() const {
std::vector<size_t> eid; eid.reserve(size());
for ( const SubgraphEdge &edge: edges_ ) {
eid.push_back(edge.index_);
}
return eid;
}
/****************************************************************************/
void Subgraph::save(const std::string &fn) const {
std::ofstream os(fn.c_str());
boost::archive::text_oarchive oa(os);
oa << *this;
os.close();
}
/****************************************************************************/
Subgraph::shared_ptr Subgraph::load(const std::string &fn) {
std::ifstream is(fn.c_str());
boost::archive::text_iarchive ia(is);
Subgraph::shared_ptr subgraph(new Subgraph());
ia >> *subgraph;
is.close();
return subgraph;
}
/****************************************************************************/
std::ostream &operator<<(std::ostream &os, const SubgraphEdge &edge) {
if ( edge.weight() != 1.0 )
os << edge.index() << "(" << std::setprecision(2) << edge.weight() << ")";
else
os << edge.index() ;
return os;
}
/****************************************************************************/
std::ostream &operator<<(std::ostream &os, const Subgraph &subgraph) {
os << "Subgraph" << endl;
for ( const SubgraphEdge &e: subgraph.edges() ) {
os << e << ", " ;
}
return os;
}
/*****************************************************************************/
void SubgraphBuilderParameters::print() const {
print(cout);
}
/***************************************************************************************/
void SubgraphBuilderParameters::print(ostream &os) const {
os << "SubgraphBuilderParameters" << endl
<< "skeleton: " << skeletonTranslator(skeleton_) << endl
<< "skeleton weight: " << skeletonWeightTranslator(skeletonWeight_) << endl
<< "augmentation weight: " << augmentationWeightTranslator(augmentationWeight_) << endl
;
}
/*****************************************************************************/
ostream& operator<<(ostream &os, const SubgraphBuilderParameters &p) {
p.print(os);
return os;
}
/*****************************************************************************/
SubgraphBuilderParameters::Skeleton SubgraphBuilderParameters::skeletonTranslator(const std::string &src){
std::string s = src; boost::algorithm::to_upper(s);
if (s == "NATURALCHAIN") return NATURALCHAIN;
else if (s == "BFS") return BFS;
else if (s == "KRUSKAL") return KRUSKAL;
throw invalid_argument("SubgraphBuilderParameters::skeletonTranslator undefined string " + s);
return KRUSKAL;
}
/****************************************************************/
std::string SubgraphBuilderParameters::skeletonTranslator(Skeleton w) {
if ( w == NATURALCHAIN )return "NATURALCHAIN";
else if ( w == BFS ) return "BFS";
else if ( w == KRUSKAL )return "KRUSKAL";
else return "UNKNOWN";
}
/****************************************************************/
SubgraphBuilderParameters::SkeletonWeight SubgraphBuilderParameters::skeletonWeightTranslator(const std::string &src) {
std::string s = src; boost::algorithm::to_upper(s);
if (s == "EQUAL") return EQUAL;
else if (s == "RHS") return RHS_2NORM;
else if (s == "LHS") return LHS_FNORM;
else if (s == "RANDOM") return RANDOM;
throw invalid_argument("SubgraphBuilderParameters::skeletonWeightTranslator undefined string " + s);
return EQUAL;
}
/****************************************************************/
std::string SubgraphBuilderParameters::skeletonWeightTranslator(SkeletonWeight w) {
if ( w == EQUAL ) return "EQUAL";
else if ( w == RHS_2NORM ) return "RHS";
else if ( w == LHS_FNORM ) return "LHS";
else if ( w == RANDOM ) return "RANDOM";
else return "UNKNOWN";
}
/****************************************************************/
SubgraphBuilderParameters::AugmentationWeight SubgraphBuilderParameters::augmentationWeightTranslator(const std::string &src) {
std::string s = src; boost::algorithm::to_upper(s);
if (s == "SKELETON") return SKELETON;
// else if (s == "STRETCH") return STRETCH;
// else if (s == "GENERALIZED_STRETCH") return GENERALIZED_STRETCH;
throw invalid_argument("SubgraphBuilder::Parameters::augmentationWeightTranslator undefined string " + s);
return SKELETON;
}
/****************************************************************/
std::string SubgraphBuilderParameters::augmentationWeightTranslator(AugmentationWeight w) {
if ( w == SKELETON ) return "SKELETON";
// else if ( w == STRETCH ) return "STRETCH";
// else if ( w == GENERALIZED_STRETCH ) return "GENERALIZED_STRETCH";
else return "UNKNOWN";
}
/****************************************************************/
std::vector<size_t> SubgraphBuilder::buildTree(const GaussianFactorGraph &gfg, const FastMap<Key, size_t> &ordering, const std::vector<double> &w) const {
const SubgraphBuilderParameters &p = parameters_;
switch (p.skeleton_) {
case SubgraphBuilderParameters::NATURALCHAIN:
return natural_chain(gfg);
break;
case SubgraphBuilderParameters::BFS:
return bfs(gfg);
break;
case SubgraphBuilderParameters::KRUSKAL:
return kruskal(gfg, ordering, w);
break;
default:
cerr << "SubgraphBuilder::buildTree undefined skeleton type" << endl;
break;
}
return vector<size_t>();
}
/****************************************************************/
std::vector<size_t> SubgraphBuilder::unary(const GaussianFactorGraph &gfg) const {
std::vector<size_t> result ;
size_t idx = 0;
for ( const GaussianFactor::shared_ptr &gf: gfg ) {
if ( gf->size() == 1 ) {
result.push_back(idx);
}
idx++;
}
return result;
}
/****************************************************************/
std::vector<size_t> SubgraphBuilder::natural_chain(const GaussianFactorGraph &gfg) const {
std::vector<size_t> result ;
size_t idx = 0;
for ( const GaussianFactor::shared_ptr &gf: gfg ) {
if ( gf->size() == 2 ) {
const Key k0 = gf->keys()[0], k1 = gf->keys()[1];
if ( (k1-k0) == 1 || (k0-k1) == 1 )
result.push_back(idx);
}
idx++;
}
return result;
}
/****************************************************************/
std::vector<size_t> SubgraphBuilder::bfs(const GaussianFactorGraph &gfg) const {
const VariableIndex variableIndex(gfg);
/* start from the first key of the first factor */
Key seed = gfg[0]->keys()[0];
const size_t n = variableIndex.size();
/* each vertex has self as the predecessor */
std::vector<size_t> result;
result.reserve(n-1);
/* Initialize */
std::queue<size_t> q;
q.push(seed);
std::set<size_t> flags;
flags.insert(seed);
/* traversal */
while ( !q.empty() ) {
const size_t head = q.front(); q.pop();
for ( const size_t id: variableIndex[head] ) {
const GaussianFactor &gf = *gfg[id];
for ( const size_t key: gf.keys() ) {
if ( flags.count(key) == 0 ) {
q.push(key);
flags.insert(key);
result.push_back(id);
}
}
}
}
return result;
}
/****************************************************************/
std::vector<size_t> SubgraphBuilder::kruskal(const GaussianFactorGraph &gfg, const FastMap<Key, size_t> &ordering, const std::vector<double> &w) const {
const VariableIndex variableIndex(gfg);
const size_t n = variableIndex.size();
const vector<size_t> idx = sort_idx(w) ;
/* initialize buffer */
vector<size_t> result;
result.reserve(n-1);
// container for acsendingly sorted edges
DSFVector D(n) ;
size_t count = 0 ; double sum = 0.0 ;
for (const size_t id: idx) {
const GaussianFactor &gf = *gfg[id];
if ( gf.keys().size() != 2 ) continue;
const size_t u = ordering.find(gf.keys()[0])->second,
u_root = D.find(u),
v = ordering.find(gf.keys()[1])->second,
v_root = D.find(v) ;
if ( u_root != v_root ) {
D.merge(u_root, v_root) ;
result.push_back(id) ;
sum += w[id] ;
if ( ++count == n-1 ) break ;
}
}
return result;
}
/****************************************************************/
std::vector<size_t> SubgraphBuilder::sample(const std::vector<double> &weights, const size_t t) const {
return uniqueSampler(weights, t);
}
/****************************************************************/
Subgraph::shared_ptr SubgraphBuilder::operator() (const GaussianFactorGraph &gfg) const {
const SubgraphBuilderParameters &p = parameters_;
const Ordering inverse_ordering = Ordering::Natural(gfg);
const FastMap<Key, size_t> forward_ordering = inverse_ordering.invert();
const size_t n = inverse_ordering.size(), t = n * p.complexity_ ;
vector<double> w = weights(gfg);
const vector<size_t> tree = buildTree(gfg, forward_ordering, w);
/* sanity check */
if ( tree.size() != n-1 ) {
throw runtime_error("SubgraphBuilder::operator() tree.size() != n-1 failed ");
}
/* down weight the tree edges to zero */
for ( const size_t id: tree ) {
w[id] = 0.0;
}
/* decide how many edges to augment */
std::vector<size_t> offTree = sample(w, t);
vector<size_t> subgraph = unary(gfg);
subgraph.insert(subgraph.end(), tree.begin(), tree.end());
subgraph.insert(subgraph.end(), offTree.begin(), offTree.end());
return boost::make_shared<Subgraph>(subgraph);
}
/****************************************************************/
SubgraphBuilder::Weights SubgraphBuilder::weights(const GaussianFactorGraph &gfg) const
{
const size_t m = gfg.size() ;
Weights weight; weight.reserve(m);
for(const GaussianFactor::shared_ptr &gf: gfg ) {
switch ( parameters_.skeletonWeight_ ) {
case SubgraphBuilderParameters::EQUAL:
weight.push_back(1.0);
break;
case SubgraphBuilderParameters::RHS_2NORM:
{
if ( JacobianFactor::shared_ptr jf = boost::dynamic_pointer_cast<JacobianFactor>(gf) ) {
weight.push_back(jf->getb().norm());
}
else if ( HessianFactor::shared_ptr hf = boost::dynamic_pointer_cast<HessianFactor>(gf) ) {
weight.push_back(hf->linearTerm().norm());
}
}
break;
case SubgraphBuilderParameters::LHS_FNORM:
{
if ( JacobianFactor::shared_ptr jf = boost::dynamic_pointer_cast<JacobianFactor>(gf) ) {
weight.push_back(std::sqrt(jf->getA().squaredNorm()));
}
else if ( HessianFactor::shared_ptr hf = boost::dynamic_pointer_cast<HessianFactor>(gf) ) {
weight.push_back(std::sqrt(hf->information().squaredNorm()));
}
}
break;
case SubgraphBuilderParameters::RANDOM:
weight.push_back(std::rand()%100 + 1.0);
break;
default:
throw invalid_argument("SubgraphBuilder::weights: undefined weight scheme ");
break;
}
}
return weight;
}
/* ************************************************************************* */
SubgraphPreconditioner::SubgraphPreconditioner(const SubgraphPreconditionerParameters &p) :
parameters_(p) {}
@ -495,7 +129,6 @@ VectorValues SubgraphPreconditioner::gradient(const VectorValues& y) const {
/* ************************************************************************* */
// Apply operator A, A*y = [I;A2*inv(R1)]*y = [y; A2*inv(R1)*y]
Errors SubgraphPreconditioner::operator*(const VectorValues& y) const {
Errors e(y);
VectorValues x = Rc1()->backSubstitute(y); /* x=inv(R1)*y */
Errors e2 = *Ab2() * x; /* A2*x */
@ -508,8 +141,10 @@ Errors SubgraphPreconditioner::operator*(const VectorValues& y) const {
void SubgraphPreconditioner::multiplyInPlace(const VectorValues& y, Errors& e) const {
Errors::iterator ei = e.begin();
for (size_t i = 0; i < y.size(); ++i, ++ei)
*ei = y[i];
for(const auto& key_value: y) {
*ei = key_value.second;
++ei;
}
// Add A2 contribution
VectorValues x = Rc1()->backSubstitute(y); // x=inv(R1)*y
@ -522,8 +157,10 @@ VectorValues SubgraphPreconditioner::operator^(const Errors& e) const {
Errors::const_iterator it = e.begin();
VectorValues y = zero();
for (size_t i = 0; i < y.size(); ++i, ++it)
y[i] = *it;
for(auto& key_value: y) {
key_value.second = *it;
++it;
}
transposeMultiplyAdd2(1.0, it, e.end(), y);
return y;
}
@ -534,9 +171,10 @@ void SubgraphPreconditioner::transposeMultiplyAdd
(double alpha, const Errors& e, VectorValues& y) const {
Errors::const_iterator it = e.begin();
for (size_t i = 0; i < y.size(); ++i, ++it) {
for(auto& key_value: y) {
const Vector& ei = *it;
axpy(alpha, ei, y[i]);
axpy(alpha, ei, key_value.second);
++it;
}
transposeMultiplyAdd2(alpha, it, e.end(), y);
}
@ -563,47 +201,51 @@ void SubgraphPreconditioner::print(const std::string& s) const {
}
/*****************************************************************************/
void SubgraphPreconditioner::solve(const Vector& y, Vector &x) const
{
/* copy first */
std::copy(y.data(), y.data() + y.rows(), x.data());
void SubgraphPreconditioner::solve(const Vector &y, Vector &x) const {
assert(x.size() == y.size());
/* in place back substitute */
for (auto cg: boost::adaptors::reverse(*Rc1_)) {
/* back substitute */
for (const auto &cg : boost::adaptors::reverse(*Rc1_)) {
/* collect a subvector of x that consists of the parents of cg (S) */
const Vector xParent = getSubvector(x, keyInfo_, KeyVector(cg->beginParents(), cg->endParents()));
const Vector rhsFrontal = getSubvector(x, keyInfo_, KeyVector(cg->beginFrontals(), cg->endFrontals()));
const KeyVector parentKeys(cg->beginParents(), cg->endParents());
const KeyVector frontalKeys(cg->beginFrontals(), cg->endFrontals());
const Vector xParent = getSubvector(x, keyInfo_, parentKeys);
const Vector rhsFrontal = getSubvector(y, keyInfo_, frontalKeys);
/* compute the solution for the current pivot */
const Vector solFrontal = cg->get_R().triangularView<Eigen::Upper>().solve(rhsFrontal - cg->get_S() * xParent);
const Vector solFrontal = cg->R().triangularView<Eigen::Upper>().solve(
rhsFrontal - cg->S() * xParent);
/* assign subvector of sol to the frontal variables */
setSubvector(solFrontal, keyInfo_, KeyVector(cg->beginFrontals(), cg->endFrontals()), x);
setSubvector(solFrontal, keyInfo_, frontalKeys, x);
}
}
/*****************************************************************************/
void SubgraphPreconditioner::transposeSolve(const Vector& y, Vector& x) const
{
void SubgraphPreconditioner::transposeSolve(const Vector &y, Vector &x) const {
/* copy first */
assert(x.size() == y.size());
std::copy(y.data(), y.data() + y.rows(), x.data());
/* in place back substitute */
for(const boost::shared_ptr<GaussianConditional> & cg: *Rc1_) {
const Vector rhsFrontal = getSubvector(x, keyInfo_, KeyVector(cg->beginFrontals(), cg->endFrontals()));
// const Vector solFrontal = cg->get_R().triangularView<Eigen::Upper>().transpose().solve(rhsFrontal);
const Vector solFrontal = cg->get_R().transpose().triangularView<Eigen::Lower>().solve(rhsFrontal);
for (const auto &cg : *Rc1_) {
const KeyVector frontalKeys(cg->beginFrontals(), cg->endFrontals());
const Vector rhsFrontal = getSubvector(x, keyInfo_, frontalKeys);
const Vector solFrontal =
cg->R().transpose().triangularView<Eigen::Lower>().solve(
rhsFrontal);
// Check for indeterminant solution
if ( solFrontal.hasNaN()) throw IndeterminantLinearSystemException(cg->keys().front());
if (solFrontal.hasNaN())
throw IndeterminantLinearSystemException(cg->keys().front());
/* assign subvector of sol to the frontal variables */
setSubvector(solFrontal, keyInfo_, KeyVector(cg->beginFrontals(), cg->endFrontals()), x);
setSubvector(solFrontal, keyInfo_, frontalKeys, x);
/* substract from parent variables */
for (GaussianConditional::const_iterator it = cg->beginParents(); it != cg->endParents(); it++) {
KeyInfo::const_iterator it2 = keyInfo_.find(*it);
Eigen::Map<Vector> rhsParent(x.data()+it2->second.colstart(), it2->second.dim(), 1);
for (auto it = cg->beginParents(); it != cg->endParents(); it++) {
const KeyInfoEntry &entry = keyInfo_.find(*it)->second;
Eigen::Map<Vector> rhsParent(x.data() + entry.start, entry.dim, 1);
rhsParent -= Matrix(cg->getA(it)).transpose() * solFrontal;
}
}
@ -613,67 +255,18 @@ void SubgraphPreconditioner::transposeSolve(const Vector& y, Vector& x) const
void SubgraphPreconditioner::build(const GaussianFactorGraph &gfg, const KeyInfo &keyInfo, const std::map<Key,Vector> &lambda)
{
/* identify the subgraph structure */
const SubgraphBuilder builder(parameters_.builderParams_);
Subgraph::shared_ptr subgraph = builder(gfg);
const SubgraphBuilder builder(parameters_.builderParams);
auto subgraph = builder(gfg);
keyInfo_ = keyInfo;
/* build factor subgraph */
GaussianFactorGraph::shared_ptr gfg_subgraph = buildFactorSubgraph(gfg, *subgraph, true);
GaussianFactorGraph::shared_ptr gfg_subgraph = buildFactorSubgraph(gfg, subgraph, true);
/* factorize and cache BayesNet */
Rc1_ = gfg_subgraph->eliminateSequential();
}
/*****************************************************************************/
Vector getSubvector(const Vector &src, const KeyInfo &keyInfo, const KeyVector &keys) {
/* a cache of starting index and dim */
typedef vector<pair<size_t, size_t> > Cache;
Cache cache;
/* figure out dimension by traversing the keys */
size_t d = 0;
for ( const Key &key: keys ) {
const KeyInfoEntry &entry = keyInfo.find(key)->second;
cache.push_back(make_pair(entry.colstart(), entry.dim()));
d += entry.dim();
}
/* use the cache to fill the result */
Vector result = Vector::Zero(d, 1);
size_t idx = 0;
for ( const Cache::value_type &p: cache ) {
result.segment(idx, p.second) = src.segment(p.first, p.second) ;
idx += p.second;
}
return result;
}
/*****************************************************************************/
void setSubvector(const Vector &src, const KeyInfo &keyInfo, const KeyVector &keys, Vector &dst) {
/* use the cache */
size_t idx = 0;
for ( const Key &key: keys ) {
const KeyInfoEntry &entry = keyInfo.find(key)->second;
dst.segment(entry.colstart(), entry.dim()) = src.segment(idx, entry.dim()) ;
idx += entry.dim();
}
}
/*****************************************************************************/
boost::shared_ptr<GaussianFactorGraph>
buildFactorSubgraph(const GaussianFactorGraph &gfg, const Subgraph &subgraph, const bool clone) {
GaussianFactorGraph::shared_ptr result(new GaussianFactorGraph());
result->reserve(subgraph.size());
for ( const SubgraphEdge &e: subgraph ) {
const size_t idx = e.index();
if ( clone ) result->push_back(gfg[idx]->clone());
else result->push_back(gfg[idx]);
}
return result;
}
} // nsamespace gtsam

204
gtsam/linear/SubgraphPreconditioner.h
View File

@ -1,6 +1,6 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
@ -17,30 +17,16 @@
#pragma once
#include <gtsam/linear/SubgraphBuilder.h>
#include <gtsam/linear/Errors.h>
#include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/IterativeSolver.h>
#include <gtsam/linear/Preconditioner.h>
#include <gtsam/linear/VectorValues.h>
#include <gtsam/base/FastMap.h>
#include <gtsam/base/FastVector.h>
#include <gtsam/base/types.h>
#include <gtsam/base/Vector.h>
#include <gtsam/dllexport.h>
#include <boost/serialization/nvp.hpp>
#include <boost/shared_ptr.hpp>
#include <map>
#include <utility>
#include <vector>
namespace boost {
namespace serialization {
class access;
} /* namespace serialization */
} /* namespace boost */
namespace gtsam {
@ -49,142 +35,11 @@ namespace gtsam {
class GaussianFactorGraph;
class VectorValues;
struct GTSAM_EXPORT SubgraphEdge {
size_t index_; /* edge id */
double weight_; /* edge weight */
SubgraphEdge() : index_(0), weight_(1.0) {}
SubgraphEdge(const SubgraphEdge &e) : index_(e.index()), weight_(e.weight()) {}
SubgraphEdge(const size_t index, const double weight = 1.0): index_(index), weight_(weight) {}
inline size_t index() const { return index_; }
inline double weight() const { return weight_; }
inline bool isUnitWeight() const { return (weight_ == 1.0); }
friend std::ostream &operator<<(std::ostream &os, const SubgraphEdge &edge);
private:
friend class boost::serialization::access;
template<class Archive>
void serialize(Archive & ar, const unsigned int /*version*/) {
ar & BOOST_SERIALIZATION_NVP(index_);
ar & BOOST_SERIALIZATION_NVP(weight_);
}
};
/**************************************************************************/
class GTSAM_EXPORT Subgraph {
public:
typedef boost::shared_ptr<Subgraph> shared_ptr;
typedef std::vector<shared_ptr> vector_shared_ptr;
typedef std::vector<SubgraphEdge> Edges;
typedef std::vector<size_t> EdgeIndices;
typedef Edges::iterator iterator;
typedef Edges::const_iterator const_iterator;
protected:
Edges edges_; /* index to the factors */
public:
Subgraph() {}
Subgraph(const Subgraph &subgraph) : edges_(subgraph.edges()) {}
Subgraph(const Edges &edges) : edges_(edges) {}
Subgraph(const std::vector<size_t> &indices) ;
inline const Edges& edges() const { return edges_; }
inline size_t size() const { return edges_.size(); }
EdgeIndices edgeIndices() const;
iterator begin() { return edges_.begin(); }
const_iterator begin() const { return edges_.begin(); }
iterator end() { return edges_.end(); }
const_iterator end() const { return edges_.end(); }
void save(const std::string &fn) const;
static shared_ptr load(const std::string &fn);
friend std::ostream &operator<<(std::ostream &os, const Subgraph &subgraph);
private:
friend class boost::serialization::access;
template<class Archive>
void serialize(Archive & ar, const unsigned int /*version*/) {
ar & BOOST_SERIALIZATION_NVP(edges_);
}
};
/****************************************************************************/
struct GTSAM_EXPORT SubgraphBuilderParameters {
public:
typedef boost::shared_ptr<SubgraphBuilderParameters> shared_ptr;
enum Skeleton {
/* augmented tree */
NATURALCHAIN = 0, /* natural ordering of the graph */
BFS, /* breadth-first search tree */
KRUSKAL, /* maximum weighted spanning tree */
} skeleton_ ;
enum SkeletonWeight { /* how to weigh the graph edges */
EQUAL = 0, /* every block edge has equal weight */
RHS_2NORM, /* use the 2-norm of the rhs */
LHS_FNORM, /* use the frobenius norm of the lhs */
RANDOM, /* bounded random edge weight */
} skeletonWeight_ ;
enum AugmentationWeight { /* how to weigh the graph edges */
SKELETON = 0, /* use the same weights in building the skeleton */
// STRETCH, /* stretch in the laplacian sense */
// GENERALIZED_STRETCH /* the generalized stretch defined in jian2013iros */
} augmentationWeight_ ;
double complexity_;
SubgraphBuilderParameters()
: skeleton_(KRUSKAL), skeletonWeight_(RANDOM), augmentationWeight_(SKELETON), complexity_(1.0) {}
virtual ~SubgraphBuilderParameters() {}
/* for serialization */
void print() const ;
virtual void print(std::ostream &os) const ;
friend std::ostream& operator<<(std::ostream &os, const PreconditionerParameters &p);
static Skeleton skeletonTranslator(const std::string &s);
static std::string skeletonTranslator(Skeleton w);
static SkeletonWeight skeletonWeightTranslator(const std::string &s);
static std::string skeletonWeightTranslator(SkeletonWeight w);
static AugmentationWeight augmentationWeightTranslator(const std::string &s);
static std::string augmentationWeightTranslator(AugmentationWeight w);
};
/*****************************************************************************/
class GTSAM_EXPORT SubgraphBuilder {
public:
typedef SubgraphBuilder Base;
typedef boost::shared_ptr<SubgraphBuilder> shared_ptr;
typedef std::vector<double> Weights;
SubgraphBuilder(const SubgraphBuilderParameters &p = SubgraphBuilderParameters())
: parameters_(p) {}
virtual ~SubgraphBuilder() {}
virtual boost::shared_ptr<Subgraph> operator() (const GaussianFactorGraph &jfg) const ;
private:
std::vector<size_t> buildTree(const GaussianFactorGraph &gfg, const FastMap<Key, size_t> &ordering, const std::vector<double> &weights) const ;
std::vector<size_t> unary(const GaussianFactorGraph &gfg) const ;
std::vector<size_t> natural_chain(const GaussianFactorGraph &gfg) const ;
std::vector<size_t> bfs(const GaussianFactorGraph &gfg) const ;
std::vector<size_t> kruskal(const GaussianFactorGraph &gfg, const FastMap<Key, size_t> &ordering, const std::vector<double> &w) const ;
std::vector<size_t> sample(const std::vector<double> &weights, const size_t t) const ;
Weights weights(const GaussianFactorGraph &gfg) const;
SubgraphBuilderParameters parameters_;
};
/*******************************************************************************************/
struct GTSAM_EXPORT SubgraphPreconditionerParameters : public PreconditionerParameters {
typedef PreconditionerParameters Base;
typedef boost::shared_ptr<SubgraphPreconditionerParameters> shared_ptr;
SubgraphPreconditionerParameters(const SubgraphBuilderParameters &p = SubgraphBuilderParameters())
: Base(), builderParams_(p) {}
virtual ~SubgraphPreconditionerParameters() {}
SubgraphBuilderParameters builderParams_;
: builderParams(p) {}
SubgraphBuilderParameters builderParams;
};
/**
@ -249,8 +104,8 @@ namespace gtsam {
/* A zero VectorValues with the structure of xbar */
VectorValues zero() const {
VectorValues V(VectorValues::Zero(*xbar_));
return V ;
assert(xbar_);
return VectorValues::Zero(*xbar_);
}
/**
@ -285,50 +140,19 @@ namespace gtsam {
/*****************************************************************************/
/* implement virtual functions of Preconditioner */
/* Computation Interfaces for Vector */
virtual void solve(const Vector& y, Vector &x) const;
virtual void transposeSolve(const Vector& y, Vector& x) const ;
/// implement x = R^{-1} y
void solve(const Vector& y, Vector &x) const override;
virtual void build(
/// implement x = R^{-T} y
void transposeSolve(const Vector& y, Vector& x) const override;
/// build/factorize the preconditioner
void build(
const GaussianFactorGraph &gfg,
const KeyInfo &info,
const std::map<Key,Vector> &lambda
) ;
) override;
/*****************************************************************************/
};
/* get subvectors */
Vector getSubvector(const Vector &src, const KeyInfo &keyInfo, const KeyVector &keys);
/* set subvectors */
void setSubvector(const Vector &src, const KeyInfo &keyInfo, const KeyVector &keys, Vector &dst);
/* build a factor subgraph, which is defined as a set of weighted edges (factors) */
boost::shared_ptr<GaussianFactorGraph>
buildFactorSubgraph(const GaussianFactorGraph &gfg, const Subgraph &subgraph, const bool clone);
/* sort the container and return permutation index with default comparator */
template <typename Container>
std::vector<size_t> sort_idx(const Container &src)
{
typedef typename Container::value_type T;
const size_t n = src.size() ;
std::vector<std::pair<size_t,T> > tmp;
tmp.reserve(n);
for ( size_t i = 0 ; i < n ; i++ )
tmp.push_back(std::make_pair(i, src[i]));
/* sort */
std::stable_sort(tmp.begin(), tmp.end()) ;
/* copy back */
std::vector<size_t> idx; idx.reserve(n);
for ( size_t i = 0 ; i < n ; i++ ) {
idx.push_back(tmp[i].first) ;
}
return idx;
}
} // namespace gtsam

175
gtsam/linear/SubgraphSolver.cpp
View File

@ -18,153 +18,94 @@
*/
#include <gtsam/linear/SubgraphSolver.h>
#include <gtsam/linear/SubgraphBuilder.h>
#include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/iterative-inl.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/SubgraphPreconditioner.h>
#include <gtsam/base/DSFVector.h>
using namespace std;
namespace gtsam {
/**************************************************************************************************/
SubgraphSolver::SubgraphSolver(const GaussianFactorGraph &gfg,
// Just taking system [A|b]
SubgraphSolver::SubgraphSolver(const GaussianFactorGraph &Ab,
const Parameters &parameters, const Ordering& ordering) :
parameters_(parameters), ordering_(ordering) {
initialize(gfg);
parameters_(parameters) {
GaussianFactorGraph::shared_ptr Ab1,Ab2;
std::tie(Ab1, Ab2) = splitGraph(Ab);
if (parameters_.verbosity())
cout << "Split A into (A1) " << Ab1->size() << " and (A2) " << Ab2->size()
<< " factors" << endl;
auto Rc1 = Ab1->eliminateSequential(ordering, EliminateQR);
auto xbar = boost::make_shared<VectorValues>(Rc1->optimize());
pc_ = boost::make_shared<SubgraphPreconditioner>(Ab2, Rc1, xbar);
}
/**************************************************************************************************/
SubgraphSolver::SubgraphSolver(const GaussianFactorGraph::shared_ptr &jfg,
const Parameters &parameters, const Ordering& ordering) :
parameters_(parameters), ordering_(ordering) {
initialize(*jfg);
// Taking eliminated tree [R1|c] and constraint graph [A2|b2]
SubgraphSolver::SubgraphSolver(const GaussianBayesNet::shared_ptr &Rc1,
const GaussianFactorGraph::shared_ptr &Ab2,
const Parameters &parameters)
: parameters_(parameters) {
auto xbar = boost::make_shared<VectorValues>(Rc1->optimize());
pc_ = boost::make_shared<SubgraphPreconditioner>(Ab2, Rc1, xbar);
}
/**************************************************************************************************/
// Taking subgraphs [A1|b1] and [A2|b2]
// delegate up
SubgraphSolver::SubgraphSolver(const GaussianFactorGraph &Ab1,
const GaussianFactorGraph &Ab2, const Parameters &parameters,
const Ordering& ordering) :
parameters_(parameters), ordering_(ordering) {
GaussianBayesNet::shared_ptr Rc1 = Ab1.eliminateSequential(ordering_,
EliminateQR);
initialize(Rc1, boost::make_shared<GaussianFactorGraph>(Ab2));
}
/**************************************************************************************************/
SubgraphSolver::SubgraphSolver(const GaussianFactorGraph::shared_ptr &Ab1,
const GaussianFactorGraph::shared_ptr &Ab2, const Parameters &parameters,
const Ordering& ordering) :
parameters_(parameters), ordering_(ordering) {
GaussianBayesNet::shared_ptr Rc1 = Ab1->eliminateSequential(ordering_,
EliminateQR);
initialize(Rc1, Ab2);
}
const GaussianFactorGraph::shared_ptr &Ab2,
const Parameters &parameters,
const Ordering &ordering)
: SubgraphSolver(Ab1.eliminateSequential(ordering, EliminateQR), Ab2,
parameters) {}
/**************************************************************************************************/
// deprecated variants
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
SubgraphSolver::SubgraphSolver(const GaussianBayesNet::shared_ptr &Rc1,
const GaussianFactorGraph &Ab2, const Parameters &parameters,
const Ordering& ordering) :
parameters_(parameters), ordering_(ordering) {
initialize(Rc1, boost::make_shared<GaussianFactorGraph>(Ab2));
}
const GaussianFactorGraph &Ab2,
const Parameters &parameters)
: SubgraphSolver(Rc1, boost::make_shared<GaussianFactorGraph>(Ab2),
parameters) {}
SubgraphSolver::SubgraphSolver(const GaussianFactorGraph &Ab1,
const GaussianFactorGraph &Ab2,
const Parameters &parameters,
const Ordering &ordering)
: SubgraphSolver(Ab1, boost::make_shared<GaussianFactorGraph>(Ab2),
parameters, ordering) {}
#endif
/**************************************************************************************************/
SubgraphSolver::SubgraphSolver(const GaussianBayesNet::shared_ptr &Rc1,
const GaussianFactorGraph::shared_ptr &Ab2, const Parameters &parameters,
const Ordering& ordering) :
parameters_(parameters), ordering_(ordering) {
initialize(Rc1, Ab2);
}
/**************************************************************************************************/
VectorValues SubgraphSolver::optimize() {
VectorValues SubgraphSolver::optimize() const {
VectorValues ybar = conjugateGradients<SubgraphPreconditioner, VectorValues,
Errors>(*pc_, pc_->zero(), parameters_);
return pc_->x(ybar);
}
/**************************************************************************************************/
VectorValues SubgraphSolver::optimize(const VectorValues &initial) {
// the initial is ignored in this case ...
return optimize();
}
/**************************************************************************************************/
void SubgraphSolver::initialize(const GaussianFactorGraph &jfg) {
GaussianFactorGraph::shared_ptr Ab1 =
boost::make_shared<GaussianFactorGraph>(), Ab2 = boost::make_shared<
GaussianFactorGraph>();
boost::tie(Ab1, Ab2) = splitGraph(jfg);
if (parameters_.verbosity())
cout << "Split A into (A1) " << Ab1->size() << " and (A2) " << Ab2->size()
<< " factors" << endl;
GaussianBayesNet::shared_ptr Rc1 = Ab1->eliminateSequential(ordering_,
EliminateQR);
VectorValues::shared_ptr xbar = boost::make_shared<VectorValues>(
Rc1->optimize());
pc_ = boost::make_shared<SubgraphPreconditioner>(Ab2, Rc1, xbar);
}
/**************************************************************************************************/
void SubgraphSolver::initialize(const GaussianBayesNet::shared_ptr &Rc1,
const GaussianFactorGraph::shared_ptr &Ab2) {
VectorValues::shared_ptr xbar = boost::make_shared<VectorValues>(
Rc1->optimize());
pc_ = boost::make_shared<SubgraphPreconditioner>(Ab2, Rc1, xbar);
}
/**************************************************************************************************/
boost::tuple<GaussianFactorGraph::shared_ptr, GaussianFactorGraph::shared_ptr> //
SubgraphSolver::splitGraph(const GaussianFactorGraph &jfg) {
const VariableIndex index(jfg);
const size_t n = index.size();
DSFVector D(n);
GaussianFactorGraph::shared_ptr At(new GaussianFactorGraph());
GaussianFactorGraph::shared_ptr Ac(new GaussianFactorGraph());
size_t t = 0;
for ( const GaussianFactor::shared_ptr &gf: jfg ) {
if (gf->keys().size() > 2) {
throw runtime_error(
"SubgraphSolver::splitGraph the graph is not simple, sanity check failed ");
}
bool augment = false;
/* check whether this factor should be augmented to the "tree" graph */
if (gf->keys().size() == 1)
augment = true;
else {
const Key u = gf->keys()[0], v = gf->keys()[1], u_root = D.find(u),
v_root = D.find(v);
if (u_root != v_root) {
t++;
augment = true;
D.merge(u_root, v_root);
}
}
if (augment)
At->push_back(gf);
else
Ac->push_back(gf);
}
return boost::tie(At, Ac);
}
/****************************************************************************/
VectorValues SubgraphSolver::optimize(const GaussianFactorGraph &gfg,
const KeyInfo &keyInfo, const std::map<Key, Vector> &lambda,
const KeyInfo &keyInfo, const map<Key, Vector> &lambda,
const VectorValues &initial) {
return VectorValues();
}
/**************************************************************************************************/
pair<GaussianFactorGraph::shared_ptr, GaussianFactorGraph::shared_ptr> //
SubgraphSolver::splitGraph(const GaussianFactorGraph &factorGraph) {
/* identify the subgraph structure */
const SubgraphBuilder builder(parameters_.builderParams);
auto subgraph = builder(factorGraph);
/* build factor subgraph */
return splitFactorGraph(factorGraph, subgraph);
}
/****************************************************************************/
} // \namespace gtsam

134
gtsam/linear/SubgraphSolver.h
View File

@ -20,6 +20,10 @@
#pragma once
#include <gtsam/linear/ConjugateGradientSolver.h>
#include <gtsam/linear/SubgraphBuilder.h>
#include <map>
#include <utility> // pair
namespace gtsam {
@ -28,30 +32,36 @@ class GaussianFactorGraph;
class GaussianBayesNet;
class SubgraphPreconditioner;
class GTSAM_EXPORT SubgraphSolverParameters: public ConjugateGradientParameters {
public:
typedef ConjugateGradientParameters Base;
SubgraphSolverParameters() :
Base() {
}
void print() const {
Base::print();
}
struct GTSAM_EXPORT SubgraphSolverParameters
: public ConjugateGradientParameters {
SubgraphBuilderParameters builderParams;
explicit SubgraphSolverParameters(const SubgraphBuilderParameters &p = SubgraphBuilderParameters())
: builderParams(p) {}
void print() const { Base::print(); }
virtual void print(std::ostream &os) const {
Base::print(os);
}
};
/**
* This class implements the SPCG solver presented in Dellaert et al in IROS'10.
* This class implements the linear SPCG solver presented in Dellaert et al in
* IROS'10.
*
* Given a linear least-squares problem \f$ f(x) = |A x - b|^2 \f$. We split the problem into
* \f$ f(x) = |A_t - b_t|^2 + |A_c - b_c|^2 \f$ where \f$ A_t \f$ denotes the "tree" part, and \f$ A_c \f$ denotes the "constraint" part.
* \f$ A_t \f$ is factorized into \f$ Q_t R_t \f$, and we compute \f$ c_t = Q_t^{-1} b_t \f$, and \f$ x_t = R_t^{-1} c_t \f$ accordingly.
* Then we solve a reparametrized problem \f$ f(y) = |y|^2 + |A_c R_t^{-1} y - \bar{b_y}|^2 \f$, where \f$ y = R_t(x - x_t) \f$, and \f$ \bar{b_y} = (b_c - A_c x_t) \f$
* Given a linear least-squares problem \f$ f(x) = |A x - b|^2 \f$. We split the
* problem into \f$ f(x) = |A_t - b_t|^2 + |A_c - b_c|^2 \f$ where \f$ A_t \f$
* denotes the "tree" part, and \f$ A_c \f$ denotes the "constraint" part.
*
* In the matrix form, it is equivalent to solving \f$ [A_c R_t^{-1} ; I ] y = [\bar{b_y} ; 0] \f$. We can solve it
* with the least-squares variation of the conjugate gradient method.
* \f$A_t \f$ is factorized into \f$ Q_t R_t \f$, and we compute
* \f$ c_t = Q_t^{-1} b_t \f$, and \f$ x_t = R_t^{-1} c_t \f$ accordingly.
*
* Then we solve a reparametrized problem
* \f$ f(y) = |y|^2 + |A_c R_t^{-1} y -\bar{b_y}|^2 \f$,
* where \f$ y = R_t(x - x_t) \f$, and \f$ \bar{b_y} = (b_c - A_c x_t) \f$
*
* In the matrix form, it is equivalent to solving
* \f$ [A_c R_t^{-1} ; I ] y = [\bar{b_y} ; 0] \f$.
* We can solve it with the least-squares variation of the conjugate gradient
* method.
*
* To use it in nonlinear optimization, please see the following example
*
@ -64,71 +74,85 @@ public:
* \nosubgrouping
*/
class GTSAM_EXPORT SubgraphSolver : public IterativeSolver {
public:
typedef SubgraphSolverParameters Parameters;
protected:
Parameters parameters_;
Ordering ordering_;
boost::shared_ptr<SubgraphPreconditioner> pc_; ///< preconditioner object
public:
/// Given a gaussian factor graph, split it into a spanning tree (A1) + others (A2) for SPCG
/// @name Constructors
/// @{
/**
* Given a gaussian factor graph, split it into a spanning tree (A1) + others
* (A2) for SPCG Will throw exception if there are ternary factors or higher
* arity, as we use Kruskal's algorithm to split the graph, treating binary
* factors as edges.
*/
SubgraphSolver(const GaussianFactorGraph &A, const Parameters &parameters,
const Ordering &ordering);
/// Shared pointer version
SubgraphSolver(const boost::shared_ptr<GaussianFactorGraph> &A,
const Parameters &parameters, const Ordering& ordering);
/**
* The user specify the subgraph part and the constraint part
* may throw exception if A1 is underdetermined
* The user specifies the subgraph part and the constraints part.
* May throw exception if A1 is underdetermined. An ordering is required to
* eliminate Ab1. We take Ab1 as a const reference, as it will be transformed
* into Rc1, but take Ab2 as a shared pointer as we need to keep it around.
*/
SubgraphSolver(const GaussianFactorGraph &Ab1, const GaussianFactorGraph &Ab2,
const Parameters &parameters, const Ordering& ordering);
/// Shared pointer version
SubgraphSolver(const boost::shared_ptr<GaussianFactorGraph> &Ab1,
SubgraphSolver(const GaussianFactorGraph &Ab1,
const boost::shared_ptr<GaussianFactorGraph> &Ab2,
const Parameters &parameters, const Ordering &ordering);
/* The same as above, but the A1 is solved before */
SubgraphSolver(const boost::shared_ptr<GaussianBayesNet> &Rc1,
const GaussianFactorGraph &Ab2, const Parameters &parameters,
const Ordering& ordering);
/// Shared pointer version
/**
* The same as above, but we assume A1 was solved by caller.
* We take two shared pointers as we keep both around.
*/
SubgraphSolver(const boost::shared_ptr<GaussianBayesNet> &Rc1,
const boost::shared_ptr<GaussianFactorGraph> &Ab2,
const Parameters &parameters, const Ordering& ordering);
const Parameters &parameters);
/// Destructor
virtual ~SubgraphSolver() {
}
virtual ~SubgraphSolver() {}
/// @}
/// @name Implement interface
/// @{
/// Optimize from zero
VectorValues optimize();
VectorValues optimize() const;
/// Optimize from given initial values
VectorValues optimize(const VectorValues &initial);
/// Interface that IterativeSolver subclasses have to implement
VectorValues optimize(const GaussianFactorGraph &gfg,
const KeyInfo &keyInfo,
const std::map<Key, Vector> &lambda,
const VectorValues &initial) override;
/** Interface that IterativeSolver subclasses have to implement */
virtual VectorValues optimize(const GaussianFactorGraph &gfg,
const KeyInfo &keyInfo, const std::map<Key, Vector> &lambda,
const VectorValues &initial);
/// @}
/// @name Implement interface
/// @{
protected:
/// Split graph using Kruskal algorithm, treating binary factors as edges.
std::pair < boost::shared_ptr<GaussianFactorGraph>,
boost::shared_ptr<GaussianFactorGraph> > splitGraph(
const GaussianFactorGraph &gfg);
void initialize(const GaussianFactorGraph &jfg);
void initialize(const boost::shared_ptr<GaussianBayesNet> &Rc1,
const boost::shared_ptr<GaussianFactorGraph> &Ab2);
/// @}
boost::tuple<boost::shared_ptr<GaussianFactorGraph>,
boost::shared_ptr<GaussianFactorGraph> >
splitGraph(const GaussianFactorGraph &gfg);
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// @name Deprecated
/// @{
SubgraphSolver(const boost::shared_ptr<GaussianFactorGraph> &A,
const Parameters &parameters, const Ordering &ordering)
: SubgraphSolver(*A, parameters, ordering) {}
SubgraphSolver(const GaussianFactorGraph &, const GaussianFactorGraph &,
const Parameters &, const Ordering &);
SubgraphSolver(const boost::shared_ptr<GaussianFactorGraph> &Ab1,
const boost::shared_ptr<GaussianFactorGraph> &Ab2,
const Parameters &parameters, const Ordering &ordering)
: SubgraphSolver(*Ab1, Ab2, parameters, ordering) {}
SubgraphSolver(const boost::shared_ptr<GaussianBayesNet> &,
const GaussianFactorGraph &, const Parameters &);
/// @}
#endif
};
} // namespace gtsam

33
gtsam/linear/VectorValues.cpp
View File

@ -51,7 +51,7 @@ namespace gtsam {
Key key;
size_t n;
boost::tie(key, n) = v;
values_.insert(make_pair(key, x.segment(j, n)));
values_.emplace(key, x.segment(j, n));
j += n;
}
}
@ -60,7 +60,7 @@ namespace gtsam {
VectorValues::VectorValues(const Vector& x, const Scatter& scatter) {
size_t j = 0;
for (const SlotEntry& v : scatter) {
values_.insert(make_pair(v.key, x.segment(j, v.dimension)));
values_.emplace(v.key, x.segment(j, v.dimension));
j += v.dimension;
}
}
@ -70,14 +70,12 @@ namespace gtsam {
{
VectorValues result;
for(const KeyValuePair& v: other)
result.values_.insert(make_pair(v.first, Vector::Zero(v.second.size())));
result.values_.emplace(v.first, Vector::Zero(v.second.size()));
return result;
}
/* ************************************************************************* */
VectorValues::iterator VectorValues::insert(const std::pair<Key, Vector>& key_value) {
// Note that here we accept a pair with a reference to the Vector, but the Vector is copied as
// it is inserted into the values_ map.
std::pair<iterator, bool> result = values_.insert(key_value);
if(!result.second)
throw std::invalid_argument(
@ -86,6 +84,16 @@ namespace gtsam {
return result.first;
}
/* ************************************************************************* */
VectorValues::iterator VectorValues::emplace(Key j, const Vector& value) {
std::pair<iterator, bool> result = values_.emplace(j, value);
if(!result.second)
throw std::invalid_argument(
"Requested to emplace variable '" + DefaultKeyFormatter(j)
+ "' already in this VectorValues.");
return result.first;
}
/* ************************************************************************* */
void VectorValues::update(const VectorValues& values)
{
@ -132,8 +140,7 @@ namespace gtsam {
bool VectorValues::equals(const VectorValues& x, double tol) const {
if(this->size() != x.size())
return false;
typedef boost::tuple<value_type, value_type> ValuePair;
for(const ValuePair& values: boost::combine(*this, x)) {
for(const auto& values: boost::combine(*this, x)) {
if(values.get<0>().first != values.get<1>().first ||
!equal_with_abs_tol(values.get<0>().second, values.get<1>().second, tol))
return false;
@ -142,12 +149,10 @@ namespace gtsam {
}
/* ************************************************************************* */
Vector VectorValues::vector() const
{
Vector VectorValues::vector() const {
// Count dimensions
DenseIndex totalDim = 0;
for(const Vector& v: *this | map_values)
totalDim += v.size();
for (const Vector& v : *this | map_values) totalDim += v.size();
// Copy vectors
Vector result(totalDim);
@ -242,7 +247,7 @@ 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)
result.values_.insert(result.end(), make_pair(j1->first, j1->second + j2->second));
result.values_.emplace(j1->first, j1->second + j2->second);
return result;
}
@ -300,7 +305,7 @@ 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)
result.values_.insert(result.end(), make_pair(j1->first, j1->second - j2->second));
result.values_.emplace(j1->first, j1->second - j2->second);
return result;
}
@ -316,7 +321,7 @@ namespace gtsam {
{
VectorValues result;
for(const VectorValues::KeyValuePair& key_v: v)
result.values_.insert(result.values_.end(), make_pair(key_v.first, a * key_v.second));
result.values_.emplace(key_v.first, a * key_v.second);
return result;
}

36
gtsam/linear/VectorValues.h
View File

@ -50,9 +50,9 @@ namespace gtsam {
* Example:
* \code
VectorValues values;
values.insert(3, Vector3(1.0, 2.0, 3.0));
values.insert(4, Vector2(4.0, 5.0));
values.insert(0, (Vector(4) << 6.0, 7.0, 8.0, 9.0).finished());
values.emplace(3, Vector3(1.0, 2.0, 3.0));
values.emplace(4, Vector2(4.0, 5.0));
values.emplace(0, (Vector(4) << 6.0, 7.0, 8.0, 9.0).finished());
// Prints [ 3.0 4.0 ]
gtsam::print(values[1]);
@ -64,18 +64,7 @@ namespace gtsam {
*
* <h2>Advanced Interface and Performance Information</h2>
*
* For advanced usage, or where speed is important:
* - Allocate space ahead of time using a pre-allocating constructor
* (\ref AdvancedConstructors "Advanced Constructors"), Zero(),
* SameStructure(), resize(), or append(). Do not use
* insert(Key, const Vector&), which always has to re-allocate the
* internal vector.
* - The vector() function permits access to the underlying Vector, for
* doing mathematical or other operations that require all values.
* - operator[]() returns a SubVector view of the underlying Vector,
* without copying any data.
*
* Access is through the variable index j, and returns a SubVector,
* Access is through the variable Key j, and returns a SubVector,
* which is a view on the underlying data structure.
*
* This class is additionally used in gradient descent and dog leg to store the gradient.
@ -183,15 +172,21 @@ namespace gtsam {
* j is already used.
* @param value The vector to be inserted.
* @param j The index with which the value will be associated. */
iterator insert(Key j, const Vector& value) {
return insert(std::make_pair(j, value));
}
iterator insert(const std::pair<Key, Vector>& key_value);
/** Emplace a vector \c value with key \c j. Throws an invalid_argument exception if the key \c
* j is already used.
* @param value The vector to be inserted.
* @param j The index with which the value will be associated. */
iterator emplace(Key j, const Vector& value);
/** Insert a vector \c value with key \c j. Throws an invalid_argument exception if the key \c
* j is already used.
* @param value The vector to be inserted.
* @param j The index with which the value will be associated. */
iterator insert(const std::pair<Key, Vector>& key_value);
iterator insert(Key j, const Vector& value) {
return insert(std::make_pair(j, value));
}
/** Insert all values from \c values. Throws an invalid_argument exception if any keys to be
* inserted are already used. */
@ -202,7 +197,8 @@ 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) {
return values_.insert(std::make_pair(j, value)); }
return values_.emplace(j, value);
}
/** Erase the vector with the given key, or throw std::out_of_range if it does not exist */
void erase(Key var) {

10
gtsam/linear/linearAlgorithms-inst.h
View File

@ -56,7 +56,7 @@ namespace gtsam
myData.parentData = parentData;
// Take any ancestor results we'll need
for(Key parent: clique->conditional_->parents())
myData.cliqueResults.insert(std::make_pair(parent, myData.parentData->cliqueResults.at(parent)));
myData.cliqueResults.emplace(parent, myData.parentData->cliqueResults.at(parent));
// Solve and store in our results
{
@ -87,10 +87,10 @@ namespace gtsam
// This is because Eigen (as of 3.3) no longer evaluates S * xS into
// a temporary, and the operation trashes valus in xS.
// See: http://eigen.tuxfamily.org/index.php?title=3.3
const Vector rhs = c.getb() - c.get_S() * xS;
const Vector rhs = c.getb() - c.S() * xS;
// TODO(gareth): Inline instantiation of Eigen::Solve and check flag
const Vector solution = c.get_R().triangularView<Eigen::Upper>().solve(rhs);
const Vector solution = c.R().triangularView<Eigen::Upper>().solve(rhs);
// Check for indeterminant solution
if(solution.hasNaN()) throw IndeterminantLinearSystemException(c.keys().front());
@ -99,8 +99,8 @@ namespace gtsam
DenseIndex vectorPosition = 0;
for(GaussianConditional::const_iterator frontal = c.beginFrontals(); frontal != c.endFrontals(); ++frontal) {
VectorValues::const_iterator r =
collectedResult.insert(*frontal, solution.segment(vectorPosition, c.getDim(frontal)));
myData.cliqueResults.insert(make_pair(r->first, r));
collectedResult.emplace(*frontal, solution.segment(vectorPosition, c.getDim(frontal)));
myData.cliqueResults.emplace(r->first, r);
vectorPosition += c.getDim(frontal);
}
}

69
gtsam/linear/tests/testGaussianBayesNet.cpp
View File

@ -21,6 +21,8 @@
#include <gtsam/base/Testable.h>
#include <gtsam/base/numericalDerivative.h>
#include <CppUnitLite/TestHarness.h>
#include <boost/tuple/tuple.hpp>
#include <boost/assign/list_of.hpp>
#include <boost/assign/std/list.hpp> // for operator +=
using namespace boost::assign;
@ -28,13 +30,11 @@ using namespace boost::assign;
// STL/C++
#include <iostream>
#include <sstream>
#include <CppUnitLite/TestHarness.h>
#include <boost/tuple/tuple.hpp>
using namespace std;
using namespace gtsam;
static const Key _x_=0, _y_=1;
static const Key _x_ = 11, _y_ = 22, _z_ = 33;
static GaussianBayesNet smallBayesNet =
list_of(GaussianConditional(_x_, Vector1::Constant(9), I_1x1, _y_, I_1x1))(
@ -42,9 +42,9 @@ static GaussianBayesNet smallBayesNet =
static GaussianBayesNet noisyBayesNet =
list_of(GaussianConditional(_x_, Vector1::Constant(9), I_1x1, _y_, I_1x1,
noiseModel::Diagonal::Sigmas(Vector1::Constant(2))))(
noiseModel::Isotropic::Sigma(1, 2.0)))(
GaussianConditional(_y_, Vector1::Constant(5), I_1x1,
noiseModel::Diagonal::Sigmas(Vector1::Constant(3))));
noiseModel::Isotropic::Sigma(1, 3.0)));
/* ************************************************************************* */
TEST( GaussianBayesNet, Matrix )
@ -136,6 +136,37 @@ TEST( GaussianBayesNet, optimize3 )
EXPECT(assert_equal(expected, actual));
}
/* ************************************************************************* */
TEST(GaussianBayesNet, ordering)
{
Ordering expected;
expected += _x_, _y_;
const auto actual = noisyBayesNet.ordering();
EXPECT(assert_equal(expected, actual));
}
/* ************************************************************************* */
TEST( GaussianBayesNet, MatrixStress )
{
GaussianBayesNet bn;
using GC = GaussianConditional;
bn.emplace_shared<GC>(_x_, Vector2(1, 2), 1 * I_2x2, _y_, 2 * I_2x2, _z_, 3 * I_2x2);
bn.emplace_shared<GC>(_y_, Vector2(3, 4), 4 * I_2x2, _z_, 5 * I_2x2);
bn.emplace_shared<GC>(_z_, Vector2(5, 6), 6 * I_2x2);
const VectorValues expected = bn.optimize();
for (const auto keys :
{KeyVector({_x_, _y_, _z_}), KeyVector({_x_, _z_, _y_}),
KeyVector({_y_, _x_, _z_}), KeyVector({_y_, _z_, _x_}),
KeyVector({_z_, _x_, _y_}), KeyVector({_z_, _y_, _x_})}) {
const Ordering ordering(keys);
Matrix R;
Vector d;
boost::tie(R, d) = bn.matrix(ordering);
EXPECT(assert_equal(expected.vector(ordering), R.inverse() * d));
}
}
/* ************************************************************************* */
TEST( GaussianBayesNet, backSubstituteTranspose )
{
@ -152,6 +183,34 @@ TEST( GaussianBayesNet, backSubstituteTranspose )
VectorValues actual = smallBayesNet.backSubstituteTranspose(x);
EXPECT(assert_equal(expected, actual));
const auto ordering = noisyBayesNet.ordering();
const Matrix R = smallBayesNet.matrix(ordering).first;
const Vector expected_vector = R.transpose().inverse() * x.vector(ordering);
EXPECT(assert_equal(expected_vector, actual.vector(ordering)));
}
/* ************************************************************************* */
TEST( GaussianBayesNet, backSubstituteTransposeNoisy )
{
// x=R'*y, expected=inv(R')*x
// 2 = 1 2
// 5 1 1 3
VectorValues
x = map_list_of<Key, Vector>
(_x_, Vector1::Constant(2))
(_y_, Vector1::Constant(5)),
expected = map_list_of<Key, Vector>
(_x_, Vector1::Constant(4))
(_y_, Vector1::Constant(9));
VectorValues actual = noisyBayesNet.backSubstituteTranspose(x);
EXPECT(assert_equal(expected, actual));
const auto ordering = noisyBayesNet.ordering();
const Matrix R = noisyBayesNet.matrix(ordering).first;
const Vector expected_vector = R.transpose().inverse() * x.vector(ordering);
EXPECT(assert_equal(expected_vector, actual.vector(ordering)));
}
/* ************************************************************************* */

16
gtsam/linear/tests/testGaussianConditional.cpp
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@ -71,33 +71,33 @@ TEST(GaussianConditional, constructor)
GaussianConditional::const_iterator it = actual.beginFrontals();
EXPECT(assert_equal(Key(1), *it));
EXPECT(assert_equal(R, actual.get_R()));
EXPECT(assert_equal(R, actual.R()));
++ it;
EXPECT(it == actual.endFrontals());
it = actual.beginParents();
EXPECT(assert_equal(Key(3), *it));
EXPECT(assert_equal(S1, actual.get_S(it)));
EXPECT(assert_equal(S1, actual.S(it)));
++ it;
EXPECT(assert_equal(Key(5), *it));
EXPECT(assert_equal(S2, actual.get_S(it)));
EXPECT(assert_equal(S2, actual.S(it)));
++ it;
EXPECT(assert_equal(Key(7), *it));
EXPECT(assert_equal(S3, actual.get_S(it)));
EXPECT(assert_equal(S3, actual.S(it)));
++it;
EXPECT(it == actual.endParents());
EXPECT(assert_equal(d, actual.get_d()));
EXPECT(assert_equal(d, actual.d()));
EXPECT(assert_equal(*s, *actual.get_model()));
// test copy constructor
GaussianConditional copied(actual);
EXPECT(assert_equal(d, copied.get_d()));
EXPECT(assert_equal(d, copied.d()));
EXPECT(assert_equal(*s, *copied.get_model()));
EXPECT(assert_equal(R, copied.get_R()));
EXPECT(assert_equal(R, copied.R()));
}
/* ************************************************************************* */
@ -212,7 +212,7 @@ TEST( GaussianConditional, solve_multifrontal )
// 3 variables, all dim=2
GaussianConditional cg(list_of(1)(2)(10), 2, blockMatrix);
EXPECT(assert_equal(Vector(blockMatrix.full().rightCols(1)), cg.get_d()));
EXPECT(assert_equal(Vector(blockMatrix.full().rightCols(1)), cg.d()));
// partial solution
Vector sl1 = Vector2(9.0, 10.0);

2
gtsam/linear/tests/testGaussianDensity.cpp
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@ -33,7 +33,7 @@ TEST(GaussianDensity, constructor)
GaussianConditional conditional(1, d, R, noiseModel::Diagonal::Sigmas(s));
GaussianDensity copied(conditional);
EXPECT(assert_equal(d, copied.get_d()));
EXPECT(assert_equal(d, copied.d()));
EXPECT(assert_equal(s, copied.get_model()->sigmas()));
}

42
gtsam/linear/tests/testJacobianFactor.cpp
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@ -162,6 +162,27 @@ TEST(JabobianFactor, Hessian_conversion) {
EXPECT(assert_equal(expected, JacobianFactor(hessian), 1e-3));
}
/* ************************************************************************* */
TEST(JabobianFactor, Hessian_conversion2) {
JacobianFactor jf(0, (Matrix(3, 3) <<
1, 2, 3,
0, 2, 3,
0, 0, 3).finished(),
Vector3(1, 2, 2));
HessianFactor hessian(jf);
EXPECT(assert_equal(jf, JacobianFactor(hessian), 1e-9));
}
/* ************************************************************************* */
TEST(JabobianFactor, Hessian_conversion3) {
JacobianFactor jf(0, (Matrix(2, 4) <<
1, 2, 3, 0,
0, 3, 2, 1).finished(),
Vector2(1, 2));
HessianFactor hessian(jf);
EXPECT(assert_equal(jf, JacobianFactor(hessian), 1e-9));
}
/* ************************************************************************* */
namespace simple_graph {
@ -322,27 +343,30 @@ TEST(JacobianFactor, matrices)
/* ************************************************************************* */
TEST(JacobianFactor, operators )
{
SharedDiagonal sigma0_1 = noiseModel::Isotropic::Sigma(2,0.1);
const double sigma = 0.1;
SharedDiagonal sigma0_1 = noiseModel::Isotropic::Sigma(2, sigma);
Matrix I = I_2x2;
Vector b = Vector2(0.2,-0.1);
JacobianFactor lf(1, -I, 2, I, b, sigma0_1);
VectorValues c;
c.insert(1, Vector2(10.,20.));
c.insert(2, Vector2(30.,60.));
VectorValues x;
Vector2 x1(10,20), x2(30,60);
x.insert(1, x1);
x.insert(2, x2);
// test A*x
Vector expectedE = Vector2(200.,400.);
Vector actualE = lf * c;
Vector expectedE = (x2 - x1)/sigma;
Vector actualE = lf * x;
EXPECT(assert_equal(expectedE, actualE));
// test A^e
VectorValues expectedX;
expectedX.insert(1, Vector2(-2000.,-4000.));
expectedX.insert(2, Vector2(2000., 4000.));
const double alpha = 0.5;
expectedX.insert(1, - alpha * expectedE /sigma);
expectedX.insert(2, alpha * expectedE /sigma);
VectorValues actualX = VectorValues::Zero(expectedX);
lf.transposeMultiplyAdd(1.0, actualE, actualX);
lf.transposeMultiplyAdd(alpha, actualE, actualX);
EXPECT(assert_equal(expectedX, actualX));
// test gradient at zero

5
gtsam/navigation/ImuFactor.cpp
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@ -52,6 +52,11 @@ void PreintegratedImuMeasurements::resetIntegration() {
//------------------------------------------------------------------------------
void PreintegratedImuMeasurements::integrateMeasurement(
const Vector3& measuredAcc, const Vector3& measuredOmega, double dt) {
if (dt <= 0) {
throw std::runtime_error(
"PreintegratedImuMeasurements::integrateMeasurement: dt <=0");
}
// Update preintegrated measurements (also get Jacobian)
Matrix9 A; // overall Jacobian wrt preintegrated measurements (df/dx)
Matrix93 B, C;

2
gtsam/navigation/ScenarioRunner.cpp
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@ -49,7 +49,7 @@ PreintegratedImuMeasurements ScenarioRunner::integrate(
NavState ScenarioRunner::predict(const PreintegratedImuMeasurements& pim,
const Bias& estimatedBias) const {
const NavState state_i(scenario_->pose(0), scenario_->velocity_n(0));
const NavState state_i(scenario_.pose(0), scenario_.velocity_n(0));
return pim.predict(state_i, estimatedBias);
}

19
gtsam/navigation/ScenarioRunner.h
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@ -42,7 +42,7 @@ class ScenarioRunner {
typedef boost::shared_ptr<PreintegratedImuMeasurements::Params> SharedParams;
private:
const Scenario* scenario_;
const Scenario& scenario_;
const SharedParams p_;
const double imuSampleTime_, sqrt_dt_;
const Bias estimatedBias_;
@ -51,7 +51,7 @@ class ScenarioRunner {
mutable Sampler gyroSampler_, accSampler_;
public:
ScenarioRunner(const Scenario* scenario, const SharedParams& p,
ScenarioRunner(const Scenario& scenario, const SharedParams& p,
double imuSampleTime = 1.0 / 100.0, const Bias& bias = Bias())
: scenario_(scenario),
p_(p),
@ -68,13 +68,13 @@ class ScenarioRunner {
// A gyro simply measures angular velocity in body frame
Vector3 actualAngularVelocity(double t) const {
return scenario_->omega_b(t);
return scenario_.omega_b(t);
}
// An accelerometer measures acceleration in body, but not gravity
Vector3 actualSpecificForce(double t) const {
Rot3 bRn(scenario_->rotation(t).transpose());
return scenario_->acceleration_b(t) - bRn * gravity_n();
Rot3 bRn(scenario_.rotation(t).transpose());
return scenario_.acceleration_b(t) - bRn * gravity_n();
}
// versions corrupted by bias and noise
@ -104,6 +104,15 @@ class ScenarioRunner {
/// Estimate covariance of sampled noise for sanity-check
Matrix6 estimateNoiseCovariance(size_t N = 1000) const;
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// @name Deprecated
/// @{
ScenarioRunner(const Scenario* scenario, const SharedParams& p,
double imuSampleTime = 1.0 / 100.0, const Bias& bias = Bias())
: ScenarioRunner(*scenario, p, imuSampleTime, bias) {}
/// @}
#endif
};
} // namespace gtsam

34
gtsam/navigation/tests/testScenarios.cpp
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@ -52,7 +52,7 @@ TEST(ScenarioRunner, Spin) {
const ConstantTwistScenario scenario(W, V);
auto p = defaultParams();
ScenarioRunner runner(&scenario, p, kDt);
ScenarioRunner runner(scenario, p, kDt);
const double T = 2 * kDt; // seconds
auto pim = runner.integrate(T);
@ -80,7 +80,7 @@ ConstantTwistScenario scenario(Z_3x1, Vector3(v, 0, 0));
/* ************************************************************************* */
TEST(ScenarioRunner, Forward) {
using namespace forward;
ScenarioRunner runner(&scenario, defaultParams(), kDt);
ScenarioRunner runner(scenario, defaultParams(), kDt);
const double T = 0.1; // seconds
auto pim = runner.integrate(T);
@ -94,7 +94,7 @@ TEST(ScenarioRunner, Forward) {
/* ************************************************************************* */
TEST(ScenarioRunner, ForwardWithBias) {
using namespace forward;
ScenarioRunner runner(&scenario, defaultParams(), kDt);
ScenarioRunner runner(scenario, defaultParams(), kDt);
const double T = 0.1; // seconds
auto pim = runner.integrate(T, kNonZeroBias);
@ -108,7 +108,7 @@ TEST(ScenarioRunner, Circle) {
const double v = 2, w = 6 * kDegree;
ConstantTwistScenario scenario(Vector3(0, 0, w), Vector3(v, 0, 0));
ScenarioRunner runner(&scenario, defaultParams(), kDt);
ScenarioRunner runner(scenario, defaultParams(), kDt);
const double T = 0.1; // seconds
auto pim = runner.integrate(T);
@ -126,7 +126,7 @@ TEST(ScenarioRunner, Loop) {
const double v = 2, w = 6 * kDegree;
ConstantTwistScenario scenario(Vector3(0, -w, 0), Vector3(v, 0, 0));
ScenarioRunner runner(&scenario, defaultParams(), kDt);
ScenarioRunner runner(scenario, defaultParams(), kDt);
const double T = 0.1; // seconds
auto pim = runner.integrate(T);
@ -157,7 +157,7 @@ const double T = 3; // seconds
/* ************************************************************************* */
TEST(ScenarioRunner, Accelerating) {
using namespace accelerating;
ScenarioRunner runner(&scenario, defaultParams(), T / 10);
ScenarioRunner runner(scenario, defaultParams(), T / 10);
auto pim = runner.integrate(T);
EXPECT(assert_equal(scenario.pose(T), runner.predict(pim).pose(), 1e-9));
@ -170,7 +170,7 @@ TEST(ScenarioRunner, Accelerating) {
/* ************************************************************************* */
TEST(ScenarioRunner, AcceleratingWithBias) {
using namespace accelerating;
ScenarioRunner runner(&scenario, defaultParams(), T / 10, kNonZeroBias);
ScenarioRunner runner(scenario, defaultParams(), T / 10, kNonZeroBias);
auto pim = runner.integrate(T, kNonZeroBias);
Matrix9 estimatedCov = runner.estimateCovariance(T, 10000, kNonZeroBias);
@ -185,7 +185,7 @@ TEST(ScenarioRunner, AcceleratingAndRotating) {
const AcceleratingScenario scenario(nRb, P0, V0, A, W);
const double T = 10; // seconds
ScenarioRunner runner(&scenario, defaultParams(), T / 10);
ScenarioRunner runner(scenario, defaultParams(), T / 10);
auto pim = runner.integrate(T);
EXPECT(assert_equal(scenario.pose(T), runner.predict(pim).pose(), 1e-9));
@ -216,7 +216,7 @@ const double T = 3; // seconds
/* ************************************************************************* */
TEST(ScenarioRunner, Accelerating2) {
using namespace accelerating2;
ScenarioRunner runner(&scenario, defaultParams(), T / 10);
ScenarioRunner runner(scenario, defaultParams(), T / 10);
auto pim = runner.integrate(T);
EXPECT(assert_equal(scenario.pose(T), runner.predict(pim).pose(), 1e-9));
@ -229,7 +229,7 @@ TEST(ScenarioRunner, Accelerating2) {
/* ************************************************************************* */
TEST(ScenarioRunner, AcceleratingWithBias2) {
using namespace accelerating2;
ScenarioRunner runner(&scenario, defaultParams(), T / 10, kNonZeroBias);
ScenarioRunner runner(scenario, defaultParams(), T / 10, kNonZeroBias);
auto pim = runner.integrate(T, kNonZeroBias);
Matrix9 estimatedCov = runner.estimateCovariance(T, 10000, kNonZeroBias);
@ -244,7 +244,7 @@ TEST(ScenarioRunner, AcceleratingAndRotating2) {
const AcceleratingScenario scenario(nRb, P0, V0, A, W);
const double T = 10; // seconds
ScenarioRunner runner(&scenario, defaultParams(), T / 10);
ScenarioRunner runner(scenario, defaultParams(), T / 10);
auto pim = runner.integrate(T);
EXPECT(assert_equal(scenario.pose(T), runner.predict(pim).pose(), 1e-9));
@ -276,7 +276,7 @@ const double T = 3; // seconds
/* ************************************************************************* */
TEST(ScenarioRunner, Accelerating3) {
using namespace accelerating3;
ScenarioRunner runner(&scenario, defaultParams(), T / 10);
ScenarioRunner runner(scenario, defaultParams(), T / 10);
auto pim = runner.integrate(T);
EXPECT(assert_equal(scenario.pose(T), runner.predict(pim).pose(), 1e-9));
@ -289,7 +289,7 @@ TEST(ScenarioRunner, Accelerating3) {
/* ************************************************************************* */
TEST(ScenarioRunner, AcceleratingWithBias3) {
using namespace accelerating3;
ScenarioRunner runner(&scenario, defaultParams(), T / 10, kNonZeroBias);
ScenarioRunner runner(scenario, defaultParams(), T / 10, kNonZeroBias);
auto pim = runner.integrate(T, kNonZeroBias);
Matrix9 estimatedCov = runner.estimateCovariance(T, 10000, kNonZeroBias);
@ -304,7 +304,7 @@ TEST(ScenarioRunner, AcceleratingAndRotating3) {
const AcceleratingScenario scenario(nRb, P0, V0, A, W);
const double T = 10; // seconds
ScenarioRunner runner(&scenario, defaultParams(), T / 10);
ScenarioRunner runner(scenario, defaultParams(), T / 10);
auto pim = runner.integrate(T);
EXPECT(assert_equal(scenario.pose(T), runner.predict(pim).pose(), 1e-9));
@ -337,7 +337,7 @@ const double T = 3; // seconds
/* ************************************************************************* */
TEST(ScenarioRunner, Accelerating4) {
using namespace accelerating4;
ScenarioRunner runner(&scenario, defaultParams(), T / 10);
ScenarioRunner runner(scenario, defaultParams(), T / 10);
auto pim = runner.integrate(T);
EXPECT(assert_equal(scenario.pose(T), runner.predict(pim).pose(), 1e-9));
@ -350,7 +350,7 @@ TEST(ScenarioRunner, Accelerating4) {
/* ************************************************************************* */
TEST(ScenarioRunner, AcceleratingWithBias4) {
using namespace accelerating4;
ScenarioRunner runner(&scenario, defaultParams(), T / 10, kNonZeroBias);
ScenarioRunner runner(scenario, defaultParams(), T / 10, kNonZeroBias);
auto pim = runner.integrate(T, kNonZeroBias);
Matrix9 estimatedCov = runner.estimateCovariance(T, 10000, kNonZeroBias);
@ -365,7 +365,7 @@ TEST(ScenarioRunner, AcceleratingAndRotating4) {
const AcceleratingScenario scenario(nRb, P0, V0, A, W);
const double T = 10; // seconds
ScenarioRunner runner(&scenario, defaultParams(), T / 10);
ScenarioRunner runner(scenario, defaultParams(), T / 10);
auto pim = runner.integrate(T);
EXPECT(assert_equal(scenario.pose(T), runner.predict(pim).pose(), 1e-9));

10
gtsam/nonlinear/DoglegOptimizer.h
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@ -37,11 +37,11 @@ public:
VERBOSE
};
double deltaInitial; ///< The initial trust region radius (default: 1.0)
double deltaInitial; ///< The initial trust region radius (default: 10.0)
VerbosityDL verbosityDL; ///< The verbosity level for Dogleg (default: SILENT), see also NonlinearOptimizerParams::verbosity
DoglegParams() :
deltaInitial(1.0), verbosityDL(SILENT) {}
deltaInitial(10.0), verbosityDL(SILENT) {}
virtual ~DoglegParams() {}
@ -105,9 +105,9 @@ public:
/** Virtual destructor */
virtual ~DoglegOptimizer() {}
/** Perform a single iteration, returning a new NonlinearOptimizer class
* containing the updated variable assignments, which may be retrieved with
* values().
/**
* Perform a single iteration, returning GaussianFactorGraph corresponding to
* the linearized factor graph.
*/
GaussianFactorGraph::shared_ptr iterate() override;

6
gtsam/nonlinear/GaussNewtonOptimizer.h
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@ -70,9 +70,9 @@ public:
/** Virtual destructor */
virtual ~GaussNewtonOptimizer() {}
/** Perform a single iteration, returning a new NonlinearOptimizer class
* containing the updated variable assignments, which may be retrieved with
* values().
/**
* Perform a single iteration, returning GaussianFactorGraph corresponding to
* the linearized factor graph.
*/
GaussianFactorGraph::shared_ptr iterate() override;

4
gtsam/nonlinear/ISAM2-impl.cpp
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@ -246,8 +246,8 @@ void updateRgProd(const ISAM2::sharedClique& clique, const KeySet& replacedKeys,
clique->conditional()->endParents()));
// Compute R*g and S*g for this clique
Vector RSgProd = clique->conditional()->get_R() * gR +
clique->conditional()->get_S() * gS;
Vector RSgProd = clique->conditional()->R() * gR +
clique->conditional()->S() * gS;
// Write into RgProd vector
DenseIndex vectorPosition = 0;

25
gtsam/nonlinear/ISAM2.cpp
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@ -96,7 +96,7 @@ bool ISAM2::equals(const ISAM2& other, double tol) const {
}
/* ************************************************************************* */
KeySet ISAM2::getAffectedFactors(const KeyList& keys) const {
FactorIndexSet ISAM2::getAffectedFactors(const KeyList& keys) const {
static const bool debug = false;
if (debug) cout << "Getting affected factors for ";
if (debug) {
@ -106,15 +106,14 @@ KeySet ISAM2::getAffectedFactors(const KeyList& keys) const {
}
if (debug) cout << endl;
NonlinearFactorGraph allAffected;
KeySet indices;
FactorIndexSet indices;
for (const Key key : keys) {
const VariableIndex::Factors& factors(variableIndex_[key]);
indices.insert(factors.begin(), factors.end());
}
if (debug) cout << "Affected factors are: ";
if (debug) {
for (const size_t index : indices) {
for (const auto index : indices) {
cout << index << " ";
}
}
@ -132,8 +131,6 @@ GaussianFactorGraph::shared_ptr ISAM2::relinearizeAffectedFactors(
KeySet candidates = getAffectedFactors(affectedKeys);
gttoc(getAffectedFactors);
NonlinearFactorGraph nonlinearAffectedFactors;
gttic(affectedKeysSet);
// for fast lookup below
KeySet affectedKeysSet;
@ -589,7 +586,7 @@ ISAM2Result ISAM2::update(
// Remove the removed factors
NonlinearFactorGraph removeFactors;
removeFactors.reserve(removeFactorIndices.size());
for (size_t index : removeFactorIndices) {
for (const auto index : removeFactorIndices) {
removeFactors.push_back(nonlinearFactors_[index]);
nonlinearFactors_.remove(index);
if (params_.cacheLinearizedFactors) linearFactors_.remove(index);
@ -823,7 +820,7 @@ void ISAM2::marginalizeLeaves(
KeySet leafKeysRemoved;
// Keep track of factors that get summarized by removing cliques
KeySet factorIndicesToRemove;
FactorIndexSet factorIndicesToRemove;
// Remove the subtree and throw away the cliques
auto trackingRemoveSubtree = [&](const sharedClique& subtreeRoot) {
@ -937,8 +934,8 @@ void ISAM2::marginalizeLeaves(
}
}
// Create factor graph from factor indices
for (size_t i : factorsFromMarginalizedInClique_step1) {
graph.push_back(nonlinearFactors_[i]->linearize(theta_));
for (const auto index: factorsFromMarginalizedInClique_step1) {
graph.push_back(nonlinearFactors_[index]->linearize(theta_));
}
// Reeliminate the linear graph to get the marginal and discard the
@ -1011,10 +1008,10 @@ void ISAM2::marginalizeLeaves(
// Remove the factors to remove that have been summarized in the newly-added
// marginal factors
NonlinearFactorGraph removedFactors;
for (size_t i : factorIndicesToRemove) {
removedFactors.push_back(nonlinearFactors_[i]);
nonlinearFactors_.remove(i);
if (params_.cacheLinearizedFactors) linearFactors_.remove(i);
for (const auto index: factorIndicesToRemove) {
removedFactors.push_back(nonlinearFactors_[index]);
nonlinearFactors_.remove(index);
if (params_.cacheLinearizedFactors) linearFactors_.remove(index);
}
variableIndex_.remove(factorIndicesToRemove.begin(),
factorIndicesToRemove.end(), removedFactors);

2
gtsam/nonlinear/ISAM2.h
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@ -292,7 +292,7 @@ class GTSAM_EXPORT ISAM2 : public BayesTree<ISAM2Clique> {
*/
void expmapMasked(const KeySet& mask);
FastSet<Key> getAffectedFactors(const FastList<Key>& keys) const;
FactorIndexSet getAffectedFactors(const FastList<Key>& keys) const;
GaussianFactorGraph::shared_ptr relinearizeAffectedFactors(
const FastList<Key>& affectedKeys, const KeySet& relinKeys) const;
GaussianFactorGraph getCachedBoundaryFactors(const Cliques& orphans);

12
gtsam/nonlinear/ISAM2Clique.cpp
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@ -37,9 +37,9 @@ void ISAM2Clique::setEliminationResult(
gradientContribution_.resize(conditional_->cols() - 1);
// Rewrite -(R * P')'*d as -(d' * R * P')' for computational speed
// reasons
gradientContribution_ << -conditional_->get_R().transpose() *
conditional_->get_d(),
-conditional_->get_S().transpose() * conditional_->get_d();
gradientContribution_ << -conditional_->R().transpose() *
conditional_->d(),
-conditional_->S().transpose() * conditional_->d();
}
/* ************************************************************************* */
@ -141,8 +141,8 @@ void ISAM2Clique::fastBackSubstitute(VectorValues* delta) const {
// This is because Eigen (as of 3.3) no longer evaluates S * xS into
// a temporary, and the operation trashes valus in xS.
// See: http://eigen.tuxfamily.org/index.php?title=3.3
const Vector rhs = c.getb() - c.get_S() * xS;
const Vector solution = c.get_R().triangularView<Eigen::Upper>().solve(rhs);
const Vector rhs = c.getb() - c.S() * xS;
const Vector solution = c.R().triangularView<Eigen::Upper>().solve(rhs);
// Check for indeterminant solution
if (solution.hasNaN())
@ -284,7 +284,7 @@ size_t optimizeWildfireNonRecursive(const ISAM2Clique::shared_ptr& root,
/* ************************************************************************* */
void ISAM2Clique::nnz_internal(size_t* result) const {
size_t dimR = conditional_->rows();
size_t dimSep = conditional_->get_S().cols();
size_t dimSep = conditional_->S().cols();
*result += ((dimR + 1) * dimR) / 2 + dimSep * dimR;
// traverse the children
for (const auto& child : children) {

2
gtsam/nonlinear/ISAM2Result.h
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@ -31,8 +31,6 @@
namespace gtsam {
typedef FastVector<size_t> FactorIndices;
/**
* @addtogroup ISAM2
* This struct is returned from ISAM2::update() and contains information about

6
gtsam/nonlinear/LevenbergMarquardtOptimizer.h
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@ -94,9 +94,9 @@ public:
/// @name Advanced interface
/// @{
/** Perform a single iteration, returning a new NonlinearOptimizer class
* containing the updated variable assignments, which may be retrieved with
* values().
/**
* Perform a single iteration, returning GaussianFactorGraph corresponding to
* the linearized factor graph.
*/
GaussianFactorGraph::shared_ptr iterate() override;

9
gtsam/nonlinear/NonlinearConjugateGradientOptimizer.h
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@ -70,7 +70,16 @@ public:
virtual ~NonlinearConjugateGradientOptimizer() {
}
/**
* Perform a single iteration, returning GaussianFactorGraph corresponding to
* the linearized factor graph.
*/
GaussianFactorGraph::shared_ptr iterate() override;
/**
* Optimize for the maximum-likelihood estimate, returning a the optimized
* variable assignments.
*/
const Values& optimize() override;
};

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