ShonanAveraging.cpp File Reference

Shonan Averaging algorithm. More...

#include <SymEigsSolver.h>
#include <cmath>
#include <gtsam/linear/PCGSolver.h>
#include <gtsam/linear/SubgraphPreconditioner.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/NonlinearEquality.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/sfm/ShonanAveraging.h>
#include <gtsam/sfm/ShonanFactor.h>
#include <gtsam/sfm/ShonanGaugeFactor.h>
#include <gtsam/slam/FrobeniusFactor.h>
#include <gtsam/slam/KarcherMeanFactor-inl.h>
#include <Eigen/Eigenvalues>
#include <algorithm>
#include <complex>
#include <iostream>
#include <map>
#include <random>
#include <set>
#include <vector>

Include dependency graph for ShonanAveraging.cpp:

Go to the source code of this file.

Classes
struct	gtsam::MatrixProdFunctor

Namespaces
	gtsam
	traits

Functions
static BinaryMeasurement< Rot3 >	gtsam::convert (const BetweenFactor< Pose3 >::shared_ptr &f)
static BinaryMeasurement< Rot2 >	gtsam::convertPose2ToBinaryMeasurementRot2 (const BetweenFactor< Pose2 >::shared_ptr &f)
static ShonanAveraging2::Measurements	gtsam::extractRot2Measurements (const BetweenFactorPose2s &factors)
static ShonanAveraging3::Measurements	gtsam::extractRot3Measurements (const BetweenFactorPose3s &factors)
template<typename T , size_t d>
static double	gtsam::Kappa (const BinaryMeasurement< T > &measurement, const ShonanAveragingParameters< d > &parameters)
static std::mt19937	gtsam::kRandomNumberGenerator (42)
template<size_t d>
static size_t	gtsam::NrUnknowns (const typename ShonanAveraging< d >::Measurements &measurements)
static Vector	gtsam::perturb (const Vector &initialVector)
static bool	gtsam::PowerMinimumEigenValue (const Sparse &A, const Matrix &S, double &minEigenValue, Vector *minEigenVector=0, size_t *numIterations=0, size_t maxIterations=1000, double minEigenvalueNonnegativityTolerance=10e-4)
	MINIMUM EIGENVALUE COMPUTATIONS. More...
template<size_t d>
static Matrix	gtsam::RoundSolutionS (const Matrix &S)
static bool	gtsam::SparseMinimumEigenValue (const Sparse &A, const Matrix &S, double *minEigenValue, Vector *minEigenVector=0, size_t *numIterations=0, size_t maxIterations=1000, double minEigenvalueNonnegativityTolerance=10e-4, Eigen::Index numLanczosVectors=20)

Detailed Description

Shonan Averaging algorithm.

Date

March 2019 - August 2020

Author

Frank Dellaert, David Rosen, and Jing Wu

Definition in file ShonanAveraging.cpp.