#include <ShonanAveraging.h>
Inheritance diagram for gtsam::ShonanAveraging3:
Public Member Functions | |
| ShonanAveraging3 (const Measurements &measurements, const Parameters ¶meters=Parameters()) | |
| ShonanAveraging3 (std::string g2oFile, const Parameters ¶meters=Parameters()) | |
| ShonanAveraging3 (const BetweenFactorPose3s &factors, const Parameters ¶meters=Parameters()) | |
 Public Member Functions inherited from gtsam::ShonanAveraging< 3 > | |
| std::vector< BinaryMeasurement< T > > | maybeRobust (const std::vector< BinaryMeasurement< T >> &measurements, bool useRobustModel=false) const |
| Values | projectFrom (size_t p, const Values &values) const |
| Values | projectFrom (size_t p, const Values &values) const |
| Values | roundSolutionS (const Matrix &S) const |
| Values | roundSolutionS (const Matrix &S) const |
| ShonanAveraging (const Measurements &measurements, const Parameters ¶meters=Parameters()) | |
| size_t | nrUnknowns () const |
| Return number of unknowns. More... | |
| size_t | numberMeasurements () const |
| Return number of measurements. More... | |
| const BinaryMeasurement< Rot > & | measurement (size_t k) const |
| k^th binary measurement More... | |
| Measurements | makeNoiseModelRobust (const Measurements &measurements, double k=1.345) const |
| const Rot & | measured (size_t k) const |
| k^th measurement, as a Rot. More... | |
| const KeyVector & | keys (size_t k) const |
| Keys for k^th measurement, as a vector of Key values. More... | |
| double | cost (const Values &values) const |
| Values | initializeRandomly (std::mt19937 &rng) const |
| Values | initializeRandomly () const |
| Random initialization for wrapper, fixed random seed. More... | |
| std::pair< Values, double > | run (const Values &initialEstimate, size_t pMin=d, size_t pMax=10) const |
| Sparse | D () const |
| Sparse version of D. More... | |
| Matrix | denseD () const |
| Dense version of D. More... | |
| Sparse | Q () const |
| Sparse version of Q. More... | |
| Matrix | denseQ () const |
| Dense version of Q. More... | |
| Sparse | L () const |
| Sparse version of L. More... | |
| Matrix | denseL () const |
| Dense version of L. More... | |
| Sparse | computeLambda (const Matrix &S) const |
| Version that takes pxdN Stiefel manifold elements. More... | |
| Matrix | computeLambda_ (const Values &values) const |
| Dense versions of computeLambda for wrapper/testing. More... | |
| Matrix | computeLambda_ (const Matrix &S) const |
| Dense versions of computeLambda for wrapper/testing. More... | |
| Sparse | computeA (const Values &values) const |
| Compute A matrix whose Eigenvalues we will examine. More... | |
| Sparse | computeA (const Matrix &S) const |
| Version that takes pxdN Stiefel manifold elements. More... | |
| Matrix | computeA_ (const Values &values) const |
| Dense version of computeA for wrapper/testing. More... | |
| double | computeMinEigenValue (const Values &values, Vector *minEigenVector=nullptr) const |
| double | computeMinEigenValueAP (const Values &values, Vector *minEigenVector=nullptr) const |
| Values | roundSolutionS (const Matrix &S) const |
| Project pxdN Stiefel manifold matrix S to Rot3^N. More... | |
| Matrix | riemannianGradient (size_t p, const Values &values) const |
| Calculate the riemannian gradient of F(values) at values. More... | |
| Values | initializeWithDescent (size_t p, const Values &values, const Vector &minEigenVector, double minEigenValue, double gradienTolerance=1e-2, double preconditionedGradNormTolerance=1e-4) const |
| Sparse | computeLambda (const Values &values) const |
| NonlinearFactorGraph | buildGraphAt (size_t p) const |
| Values | initializeRandomlyAt (size_t p, std::mt19937 &rng) const |
| Values | initializeRandomlyAt (size_t p) const |
| Version of initializeRandomlyAt with fixed random seed. More... | |
| double | costAt (size_t p, const Values &values) const |
| std::pair< double, Vector > | computeMinEigenVector (const Values &values) const |
| bool | checkOptimality (const Values &values) const |
| std::shared_ptr< LevenbergMarquardtOptimizer > | createOptimizerAt (size_t p, const Values &initial) const |
| Values | tryOptimizingAt (size_t p, const Values &initial) const |
| Values | projectFrom (size_t p, const Values &values) const |
| Values | roundSolution (const Values &values) const |
Additional Inherited Members | |
| Public Types inherited from gtsam::ShonanAveraging< 3 > | |
| using | Measurements = std::vector< BinaryMeasurement< Rot > > |
| using | Parameters = ShonanAveragingParameters< d > |
| using | Rot = typename Parameters::Rot |
| using | Sparse = Eigen::SparseMatrix< double > |
| Static Public Member Functions inherited from gtsam::ShonanAveraging< 3 > | |
| static Matrix | StiefelElementMatrix (const Values &values) |
| Project to pxdN Stiefel manifold. More... | |
| static VectorValues | TangentVectorValues (size_t p, const Vector &v) |
| Create a VectorValues with eigenvector v_i. More... | |
| static Values | LiftwithDescent (size_t p, const Values &values, const Vector &minEigenVector) |
| static Values | LiftTo (size_t p, const Values &values) |
| Lift Values of type T to SO(p) More... | |
Detailed Description
Definition at line 438 of file ShonanAveraging.h.
Constructor & Destructor Documentation
◆ ShonanAveraging3() [1/3]
| gtsam::ShonanAveraging3::ShonanAveraging3 | ( | const Measurements & | measurements, |
| const Parameters & | parameters = Parameters() |
||
| ) |
Definition at line 981 of file ShonanAveraging.cpp.
◆ ShonanAveraging3() [2/3]
|
explicit |
Definition at line 986 of file ShonanAveraging.cpp.
◆ ShonanAveraging3() [3/3]
| gtsam::ShonanAveraging3::ShonanAveraging3 | ( | const BetweenFactorPose3s & | factors, |
| const Parameters & | parameters = Parameters() |
||
| ) |
Definition at line 1019 of file ShonanAveraging.cpp.
The documentation for this class was generated from the following files: