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gtsam::ShonanAveraging3 Class Reference
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 > | |
| 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 |
| std::vector< BinaryMeasurement< T > > | maybeRobust (const std::vector< BinaryMeasurement< T > > &measurements, bool useRobustModel=false) const |
| Helper function to convert measurements to robust noise model if flag is set. | |
| ShonanAveraging (const Measurements &measurements, const Parameters ¶meters=Parameters()) | |
| Construct from set of relative measurements (given as BetweenFactor<Rot3> for now) NoiseModel must be isotropic. | |
| size_t | nrUnknowns () const |
| Return number of unknowns. | |
| size_t | numberMeasurements () const |
| Return number of measurements. | |
| const BinaryMeasurement< Rot > & | measurement (size_t k) const |
| k^th binary measurement | |
| Measurements | makeNoiseModelRobust (const Measurements &measurements, double k=1.345) const |
| Update factors to use robust Huber loss. | |
| const Rot & | measured (size_t k) const |
| k^th measurement, as a Rot. | |
| const KeyVector & | keys (size_t k) const |
| Keys for k^th measurement, as a vector of Key values. | |
| double | cost (const Values &values) const |
| Calculate cost for SO(3) Values should be of type Rot3. | |
| Values | initializeRandomly (std::mt19937 &rng) const |
| Initialize randomly at SO(d). | |
| std::pair< Values, double > | run (const Values &initialEstimate, size_t pMin=d, size_t pMax=10) const |
| Optimize at different values of p until convergence. | |
| Sparse | D () const |
| Sparse version of D. | |
| Matrix | denseD () const |
| Dense version of D. | |
| Sparse | Q () const |
| Sparse version of Q. | |
| Matrix | denseQ () const |
| Dense version of Q. | |
| Sparse | L () const |
| Sparse version of L. | |
| Matrix | denseL () const |
| Dense version of L. | |
| Sparse | computeLambda (const Matrix &S) const |
| Version that takes pxdN Stiefel manifold elements. | |
| Matrix | computeLambda_ (const Values &values) const |
| Dense versions of computeLambda for wrapper/testing. | |
| Sparse | computeA (const Values &values) const |
| Compute A matrix whose Eigenvalues we will examine. | |
| Matrix | computeA_ (const Values &values) const |
| Dense version of computeA for wrapper/testing. | |
| double | computeMinEigenValue (const Values &values, Vector *minEigenVector=nullptr) const |
| Compute minimum eigenvalue for optimality check. | |
| double | computeMinEigenValueAP (const Values &values, Vector *minEigenVector=nullptr) const |
| Compute minimum eigenvalue with accelerated power method. | |
| Matrix | riemannianGradient (size_t p, const Values &values) const |
| Calculate the riemannian gradient of F(values) at values. | |
| Values | initializeWithDescent (size_t p, const Values &values, const Vector &minEigenVector, double minEigenValue, double gradienTolerance=1e-2, double preconditionedGradNormTolerance=1e-4) const |
| Given some values at p-1, return new values at p, by doing a line search along the descent direction, computed from the minimum eigenvector at p-1. | |
| NonlinearFactorGraph | buildGraphAt (size_t p) const |
| Build graph for SO(p). | |
| Values | initializeRandomlyAt (size_t p, std::mt19937 &rng) const |
| Create initial Values of type SO(p). | |
| double | costAt (size_t p, const Values &values) const |
| Calculate cost for SO(p) Values should be of type SO(p). | |
| std::pair< double, Vector > | computeMinEigenVector (const Values &values) const |
| Compute minimum eigenvalue for optimality check. | |
| bool | checkOptimality (const Values &values) const |
| Check optimality. | |
| boost::shared_ptr< LevenbergMarquardtOptimizer > | createOptimizerAt (size_t p, const Values &initial) const |
| Try to create optimizer at SO(p). | |
| Values | tryOptimizingAt (size_t p, const Values &initial) const |
| Try to optimize at SO(p). | |
| Values | roundSolution (const Values &values) const |
| Project from SO(p)^N to Rot2^N or Rot3^N Values should be of type SO(p). | |
Additional Inherited Members | |
| static Matrix | StiefelElementMatrix (const Values &values) |
| Project to pxdN Stiefel manifold. | |
| static VectorValues | TangentVectorValues (size_t p, const Vector &v) |
| Create a VectorValues with eigenvector v_i. | |
| static Values | LiftwithDescent (size_t p, const Values &values, const Vector &minEigenVector) |
| Lift up the dimension of values in type SO(p-1) with descent direction provided by minEigenVector and return new values in type SO(p). | |
| static Values | LiftTo (size_t p, const Values &values) |
| Lift Values of type T to SO(p). | |
| Public Types inherited from gtsam::ShonanAveraging< 3 > | |
| using | Sparse |
| using | Parameters |
| using | Rot |
| using | Measurements |
The documentation for this class was generated from the following files:
- /tmp/gtsam-4.2-docs.H5EUbA/src/gtsam/sfm/ShonanAveraging.h
- /tmp/gtsam-4.2-docs.H5EUbA/src/gtsam/sfm/ShonanAveraging.cpp
