template<size_t d>
class gtsam::ShonanAveraging< d >
Class that implements Shonan Averaging from our ECCV'20 paper.
Note: The "basic" API uses all Rot values (Rot2 or Rot3, depending on value of d), whereas the different levels and "advanced" API at SO(p) needs Values of type SOn<Dynamic>.
The template parameter d can be 2 or 3. Both are specialized in the .cpp file.
If you use this code in your work, please consider citing our paper: Shonan Rotation Averaging, Global Optimality by Surfing SO(p)^n Frank Dellaert, David M. Rosen, Jing Wu, Robert Mahony, and Luca Carlone, European Computer Vision Conference, 2020. You can view our ECCV spotlight video at https://youtu.be/5ppaqMyHtE0
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Sparse | D () const |
| Sparse version of D.
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Matrix | denseD () const |
| Dense version of D.
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Sparse | Q () const |
| Sparse version of Q.
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Matrix | denseQ () const |
| Dense version of Q.
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Sparse | L () const |
| Sparse version of L.
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Matrix | denseL () const |
| Dense version of L.
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Sparse | computeLambda (const Matrix &S) const |
| Version that takes pxdN Stiefel manifold elements.
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Matrix | computeLambda_ (const Values &values) const |
| Dense versions of computeLambda for wrapper/testing.
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Matrix | computeLambda_ (const Matrix &S) const |
| Dense versions of computeLambda for wrapper/testing.
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Sparse | computeA (const Values &values) const |
| Compute A matrix whose Eigenvalues we will examine.
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Sparse | computeA (const Matrix &S) const |
| Version that takes pxdN Stiefel manifold elements.
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Matrix | computeA_ (const Values &values) const |
| Dense version of computeA for wrapper/testing.
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double | computeMinEigenValue (const Values &values, Vector *minEigenVector=nullptr) const |
| Compute minimum eigenvalue for optimality check.
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double | computeMinEigenValueAP (const Values &values, Vector *minEigenVector=nullptr) const |
| Compute minimum eigenvalue with accelerated power method.
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Values | roundSolutionS (const Matrix &S) const |
| Project pxdN Stiefel manifold matrix S to Rot3^N.
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Matrix | riemannianGradient (size_t p, const Values &values) const |
| Calculate the riemannian gradient of F(values) at values.
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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.
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static Matrix | StiefelElementMatrix (const Values &values) |
| Project to pxdN Stiefel manifold.
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static VectorValues | TangentVectorValues (size_t p, const Vector &v) |
| Create a VectorValues with eigenvector v_i.
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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)
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NonlinearFactorGraph | buildGraphAt (size_t p) const |
| Build graph for SO(p)
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Values | initializeRandomlyAt (size_t p, std::mt19937 &rng) const |
| Create initial Values of type SO(p)
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Values | initializeRandomlyAt (size_t p) const |
| Version of initializeRandomlyAt with fixed random seed.
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double | costAt (size_t p, const Values &values) const |
| Calculate cost for SO(p) Values should be of type SO(p)
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Sparse | computeLambda (const Values &values) const |
| Given an estimated local minimum Yopt for the (possibly lifted) relaxation, this function computes and returns the block-diagonal elements of the corresponding Lagrange multiplier.
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std::pair< double, Vector > | computeMinEigenVector (const Values &values) const |
| Compute minimum eigenvalue for optimality check.
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bool | checkOptimality (const Values &values) const |
| Check optimality.
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boost::shared_ptr< LevenbergMarquardtOptimizer > | createOptimizerAt (size_t p, const Values &initial) const |
| Try to create optimizer at SO(p)
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Values | tryOptimizingAt (size_t p, const Values &initial) const |
| Try to optimize at SO(p)
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Values | projectFrom (size_t p, const Values &values) const |
| Project from SO(p) to Rot2 or Rot3 Values should be of type SO(p)
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Values | roundSolution (const Values &values) const |
| Project from SO(p)^N to Rot2^N or Rot3^N Values should be of type SO(p)
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template<class T > |
static Values | LiftTo (size_t p, const Values &values) |
| Lift Values of type T to SO(p)
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template<typename T > |
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.
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Values | projectFrom (size_t p, const Values &values) const |
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Values | projectFrom (size_t p, const Values &values) const |
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Values | roundSolutionS (const Matrix &S) const |
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Values | roundSolutionS (const Matrix &S) const |
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| ShonanAveraging (const Measurements &measurements, const Parameters ¶meters=Parameters()) |
| Construct from set of relative measurements (given as BetweenFactor<Rot3> for now) NoiseModel must be isotropic.
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size_t | nrUnknowns () const |
| Return number of unknowns.
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size_t | numberMeasurements () const |
| Return number of measurements.
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const BinaryMeasurement< Rot > & | measurement (size_t k) const |
| k^th binary measurement
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Measurements | makeNoiseModelRobust (const Measurements &measurements, double k=1.345) const |
| Update factors to use robust Huber loss.
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const Rot & | measured (size_t k) const |
| k^th measurement, as a Rot.
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const KeyVector & | keys (size_t k) const |
| Keys for k^th measurement, as a vector of Key values.
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double | cost (const Values &values) const |
| Calculate cost for SO(3) Values should be of type Rot3.
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Values | initializeRandomly (std::mt19937 &rng) const |
| Initialize randomly at SO(d)
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Values | initializeRandomly () const |
| Random initialization for wrapper, fixed random seed.
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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.
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