Added more functionality to matlab wrapper
Andrew Melim
parent
fd249efcb9
commit
43b7f98594
275
gtsam.h
275
gtsam.h
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@ -82,8 +82,38 @@
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* - TODO: Handle gtsam::Rot3M conversions to quaternions
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*/
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/*namespace std {
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template<T>
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//Module.cpp needs to be changed to allow lowercase classnames
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class vector
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{
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//Capcaity
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size_t size() const;
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size_t max_size() const;
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void resize(size_t sz, T c = T());
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size_t capacity() const;
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bool empty() const;
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void reserve(size_t n);
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//Element acces
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T& at(size_t n);
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const T& at(size_t n) const;
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T& front();
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const T& front() const;
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T& back();
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const T& back() const;
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//Modifiers
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void assign(size_t n, const T& u);
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void push_back(const T& x);
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void pop_back();
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};
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}*/
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namespace gtsam {
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//typedef std::vector<Index> IndexVector;
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//*************************************************************************
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// base
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//*************************************************************************
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@ -664,14 +694,12 @@ virtual class SimpleCamera : gtsam::Value {
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//*************************************************************************
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// inference
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//*************************************************************************
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#include <gtsam/inference/Permutation.h>
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class Permutation {
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// Standard Constructors and Named Constructors
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Permutation();
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Permutation(size_t nVars);
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static gtsam::Permutation Identity(size_t nVars);
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// FIXME: Cannot currently wrap std::vector
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//static gtsam::Permutation PullToFront(const vector<size_t>& toFront, size_t size, bool filterDuplicates);
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//static gtsam::Permutation PushToBack(const vector<size_t>& toBack, size_t size, bool filterDuplicates = false);
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// Testable
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void print(string s) const;
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@ -684,10 +712,16 @@ class Permutation {
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void resize(size_t newSize);
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gtsam::Permutation* permute(const gtsam::Permutation& permutation) const;
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gtsam::Permutation* inverse() const;
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// FIXME: Cannot currently wrap std::vector
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//static gtsam::Permutation PullToFront(const vector<size_t>& toFront, size_t size, bool filterDuplicates);
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//static gtsam::Permutation PushToBack(const vector<size_t>& toBack, size_t size, bool filterDuplicates = false);
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gtsam::Permutation* partialPermutation(const gtsam::Permutation& selector, const gtsam::Permutation& partialPermutation) const;
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};
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class IndexFactor {
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// Standard Constructors and Named Constructors
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IndexFactor(const gtsam::IndexFactor& f);
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IndexFactor(const gtsam::IndexConditional& c);
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IndexFactor();
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IndexFactor(size_t j);
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IndexFactor(size_t j1, size_t j2);
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@ -697,6 +731,7 @@ class IndexFactor {
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IndexFactor(size_t j1, size_t j2, size_t j3, size_t j4, size_t j5, size_t j6);
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// FIXME: Must wrap std::set<Index> for this to work
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//IndexFactor(const std::set<Index>& js);
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void permuteWithInverse(const gtsam::Permutation& inversePermutation);
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// From Factor
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size_t size() const;
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@ -726,6 +761,10 @@ class IndexConditional {
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size_t nrFrontals() const;
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size_t nrParents() const;
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gtsam::IndexFactor* toFactor() const;
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//Advanced interface
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bool permuteSeparatorWithInverse(const gtsam::Permutation& inversePermutation);
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void permuteWithInverse(const gtsam::Permutation& inversePermutation);
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};
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#include <gtsam/inference/SymbolicFactorGraph.h>
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@ -742,11 +781,15 @@ class SymbolicBayesNet {
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// Standard interface
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size_t size() const;
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void push_back(const gtsam::IndexConditional* conditional);
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// FIXME: cannot overload functions
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//void push_back(const SymbolicBayesNet bn);
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//TODO:Throws parse error
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//void push_back(const gtsam::SymbolicBayesNet bn);
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void push_front(const gtsam::IndexConditional* conditional);
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// FIXME: cannot overload functions
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//void push_front(const SymbolicBayesNet bn);
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//TODO:Throws parse error
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//void push_front(const gtsam::SymbolicBayesNet bn);
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//Advanced Interface
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gtsam::IndexConditional* front() const;
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gtsam::IndexConditional* back() const;
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void pop_front();
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void permuteWithInverse(const gtsam::Permutation& inversePermutation);
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bool permuteSeparatorWithInverse(const gtsam::Permutation& inversePermutation);
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@ -766,6 +809,7 @@ class SymbolicBayesTree {
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bool equals(const gtsam::SymbolicBayesTree& other, double tol) const;
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// Standard interface
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size_t findParentClique(const gtsam::IndexConditional& parents) const;
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size_t size() const;
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void saveGraph(string s) const;
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void clear();
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@ -788,7 +832,13 @@ class SymbolicFactorGraph {
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// FIXME: Must wrap FastSet<Index> for this to work
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//FastSet<Index> keys() const;
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pair<gtsam::IndexConditional*, gtsam::SymbolicFactorGraph> eliminateFrontals(size_t nFrontals) const;
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//Advanced Interface
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void push_factor(size_t key);
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void push_factor(size_t key1, size_t key2);
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void push_factor(size_t key1, size_t key2, size_t key3);
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void push_factor(size_t key1, size_t key2, size_t key3, size_t key4);
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pair<gtsam::IndexConditional*, gtsam::SymbolicFactorGraph> eliminateFrontals(size_t nFrontals) const;
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};
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#include <gtsam/inference/SymbolicSequentialSolver.h>
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@ -803,6 +853,7 @@ class SymbolicSequentialSolver {
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// Standard interface
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gtsam::SymbolicBayesNet* eliminate() const;
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gtsam::IndexFactor* marginalFactor(size_t j) const;
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};
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#include <gtsam/inference/SymbolicMultifrontalSolver.h>
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@ -817,6 +868,7 @@ class SymbolicMultifrontalSolver {
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// Standard interface
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gtsam::SymbolicBayesTree* eliminate() const;
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gtsam::IndexFactor* marginalFactor(size_t j) const;
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};
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#include <gtsam/inference/SymbolicFactorGraph.h>
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@ -824,14 +876,15 @@ class VariableIndex {
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// Standard Constructors and Named Constructors
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VariableIndex();
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// FIXME: Handle templates somehow
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//template<class FactorGraph> VariableIndex(const FactorGraph& factorGraph, size_t nVariables);
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//template<class FactorGraph> VariableIndex(const FactorGraph& factorGraph);
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//template<T = {gtsam::FactorGraph}>
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//VariableIndex(const T& factorGraph, size_t nVariables);
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//VariableIndex(const T& factorGraph);
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VariableIndex(const gtsam::SymbolicFactorGraph& factorGraph);
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VariableIndex(const gtsam::SymbolicFactorGraph& factorGraph, size_t nVariables);
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// VariableIndex(const gtsam::GaussianFactorGraph& factorGraph);
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// VariableIndex(const gtsam::GaussianFactorGraph& factorGraph, size_t nVariables);
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// VariableIndex(const gtsam::NonlinearFactorGraph& factorGraph);
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// VariableIndex(const gtsam::NonlinearFactorGraph& factorGraph, size_t nVariables);
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VariableIndex(const gtsam::GaussianFactorGraph& factorGraph);
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VariableIndex(const gtsam::GaussianFactorGraph& factorGraph, size_t nVariables);
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VariableIndex(const gtsam::NonlinearFactorGraph& factorGraph);
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VariableIndex(const gtsam::NonlinearFactorGraph& factorGraph, size_t nVariables);
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VariableIndex(const gtsam::VariableIndex& other);
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// Testable
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@ -857,7 +910,8 @@ virtual class Base {
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virtual class Gaussian : gtsam::noiseModel::Base {
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static gtsam::noiseModel::Gaussian* SqrtInformation(Matrix R);
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static gtsam::noiseModel::Gaussian* Covariance(Matrix R);
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// Matrix R() const; // FIXME: cannot parse!!!
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//Matrix R() const; // FIXME: cannot parse!!!
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bool equals(gtsam::noiseModel::Base& expected, double tol);
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void print(string s) const;
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};
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@ -869,6 +923,20 @@ virtual class Diagonal : gtsam::noiseModel::Gaussian {
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void print(string s) const;
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};
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virtual class Constrained : gtsam::noiseModel::Diagonal {
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static gtsam::noiseModel::Constrained* MixedSigmas(const Vector& mu, const Vector& sigmas);
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static gtsam::noiseModel::Constrained* MixedSigmas(double m, const Vector& sigmas);
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static gtsam::noiseModel::Constrained* MixedVariances(const Vector& mu, const Vector& variances);
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static gtsam::noiseModel::Constrained* MixedVariances(const Vector& variances);
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static gtsam::noiseModel::Constrained* MixedPrecisions(const Vector& mu, const Vector& precisions);
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static gtsam::noiseModel::Constrained* MixedPrecisions(const Vector& precisions);
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static gtsam::noiseModel::Constrained* All(size_t dim);
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static gtsam::noiseModel::Constrained* All(size_t dim, double m);
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gtsam::noiseModel::Constrained* unit() const;
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};
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virtual class Isotropic : gtsam::noiseModel::Diagonal {
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static gtsam::noiseModel::Isotropic* Sigma(size_t dim, double sigma);
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static gtsam::noiseModel::Isotropic* Variance(size_t dim, double varianace);
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@ -884,44 +952,80 @@ virtual class Unit : gtsam::noiseModel::Isotropic {
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#include <gtsam/linear/Sampler.h>
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class Sampler {
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//Constructors
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Sampler(gtsam::noiseModel::Diagonal* model, int seed);
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Sampler(Vector sigmas, int seed);
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Sampler(int seed);
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//Standard Interface
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size_t dim() const;
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Vector sigmas() const;
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gtsam::noiseModel::Diagonal* model() const;
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Vector sample();
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Vector sampleNewModel(gtsam::noiseModel::Diagonal* model);
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};
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class VectorValues {
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//Constructors
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VectorValues();
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VectorValues(const gtsam::VectorValues& other);
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VectorValues(size_t nVars, size_t varDim);
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void print(string s) const;
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bool equals(const gtsam::VectorValues& expected, double tol) const;
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//Named Constructors
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static gtsam::VectorValues Zero(const gtsam::VectorValues& model);
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static gtsam::VectorValues Zero(size_t nVars, size_t varDim);
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static gtsam::VectorValues SameStructure(const gtsam::VectorValues& other);
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//Standard Interface
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size_t size() const;
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size_t dim(size_t j) const;
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size_t dim() const;
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bool exists(size_t j) const;
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void print(string s) const;
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bool equals(const gtsam::VectorValues& expected, double tol) const;
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void insert(size_t j, Vector value);
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Vector vector() const;
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Vector at(size_t j) const;
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//Advanced Interface
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void resizeLike(const gtsam::VectorValues& other);
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void resize(size_t nVars, size_t varDim);
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void setZero();
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//FIXME: Parse errors with vector()
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//const Vector& vector() const;
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//Vector& vector();
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bool hasSameStructure(const gtsam::VectorValues& other) const;
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double dot(const gtsam::VectorValues& V) const;
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double norm() const;
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};
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class GaussianConditional {
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//Constructors
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GaussianConditional(size_t key, Vector d, Matrix R, Vector sigmas);
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GaussianConditional(size_t key, Vector d, Matrix R, size_t name1, Matrix S,
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Vector sigmas);
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GaussianConditional(size_t key, Vector d, Matrix R, size_t name1, Matrix S,
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size_t name2, Matrix T, Vector sigmas);
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//Standard Interface
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void print(string s) const;
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bool equals(const gtsam::GaussianConditional &cg, double tol) const;
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size_t dim() const;
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//Advanced Interface
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Matrix computeInformation() const;
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gtsam::JacobianFactor* toFactor() const;
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void solveInPlace(gtsam::VectorValues& x) const;
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void solveTransposeInPlace(gtsam::VectorValues& gy) const;
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void scaleFrontalsBySigma(gtsam::VectorValues& gy) const;
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};
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class GaussianDensity {
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//Constructors
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GaussianDensity(size_t key, Vector d, Matrix R, Vector sigmas);
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//Standard Interface
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void print(string s) const;
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Vector mean() const;
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Matrix information() const;
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@ -929,13 +1033,34 @@ class GaussianDensity {
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};
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class GaussianBayesNet {
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//Constructors
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GaussianBayesNet();
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//Standard Interface
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void print(string s) const;
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bool equals(const gtsam::GaussianBayesNet& cbn, double tol) const;
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void push_back(gtsam::GaussianConditional* conditional);
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void push_front(gtsam::GaussianConditional* conditional);
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};
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//Non-Class methods found in GaussianBayesNet.h
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//FIXME: No MATLAB documentation for these functions!
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/*gtsam::GaussianBayesNet scalarGaussian(size_t key, double mu, double sigma);
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gtsam::GaussianBayesNet simpleGaussian(size_t key, const Vector& mu, double sigma);
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void push_front(gtsam::GaussianBayesNet& bn, size_t key, Vector d, Matrix R, size_t name1, Matrix S, Vector sigmas);
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void push_front(gtsam::GaussianBayesNet& bn, size_t key, Vector d, Matrix R, size_t name1, Matrix S, size_t name2, Matrix T, Vector sigmas);
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gtsam::VectorValues* allocateVectorValues(const gtsam::GaussianBayesNet& bn);
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gtsam::VectorValues optimize(const gtsam::GaussianBayesNet& bn);
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void optimizeInPlace(const gtsam::GaussianBayesNet& bn, gtsam::VectorValues& x);
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gtsam::VectorValues optimizeGradientSearch(const gtsam::GaussianBayesNet& bn);
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void optimizeGradientSearchInPlace(const gtsam::GaussianBayesNet& bn, gtsam::VectorValues& grad);
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gtsam::VectorValues backSubstitute(const gtsam::GaussianBayesNet& bn, const gtsam::VectorValues& gx);
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gtsam::VectorValues backSubstituteTranspose(const gtsam::GaussianBayesNet& bn, const gtsam::VectorValues& gx);
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pair<Matrix, Vector> matrix(const gtsam::GaussianBayesNet& bn);
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double determinant(const gtsam::GaussianBayesNet& bayesNet);
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gtsam::VectorValues gradient(const gtsam::GaussianBayesNet& bayesNet, const gtsam::VectorValues& x0);
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void gradientAtZero(const gtsam::GaussianBayesNet& bayesNet, const gtsam::VectorValues& g);*/
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virtual class GaussianFactor {
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void print(string s) const;
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bool equals(const gtsam::GaussianFactor& lf, double tol) const;
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@ -945,6 +1070,7 @@ virtual class GaussianFactor {
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};
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virtual class JacobianFactor : gtsam::GaussianFactor {
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//Constructors
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JacobianFactor();
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JacobianFactor(Vector b_in);
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JacobianFactor(size_t i1, Matrix A1, Vector b,
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@ -954,21 +1080,46 @@ virtual class JacobianFactor : gtsam::GaussianFactor {
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JacobianFactor(size_t i1, Matrix A1, size_t i2, Matrix A2, size_t i3, Matrix A3,
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Vector b, const gtsam::noiseModel::Diagonal* model);
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JacobianFactor(const gtsam::GaussianFactor& factor);
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//Testable
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void print(string s) const;
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void printKeys(string s) const;
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bool equals(const gtsam::GaussianFactor& lf, double tol) const;
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size_t size() const;
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Vector unweighted_error(const gtsam::VectorValues& c) const;
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Vector error_vector(const gtsam::VectorValues& c) const;
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double error(const gtsam::VectorValues& c) const;
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gtsam::GaussianFactor* negate() const;
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bool empty() const;
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Vector getb() const;
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pair<Matrix, Vector> matrix() const;
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Matrix matrix_augmented() const;
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gtsam::GaussianConditional* eliminateFirst();
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gtsam::GaussianConditional* eliminate(size_t nrFrontals);
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//Standard Interface
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gtsam::GaussianFactor* negate() const;
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bool empty() const;
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Matrix getA() const;
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Vector getb() const;
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size_t rows() const;
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size_t cols() const;
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size_t numberOfRows() const;
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bool isConstrained() const;
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pair<Matrix, Vector> matrix() const;
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Matrix matrix_augmented() const;
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gtsam::GaussianConditional* eliminateFirst();
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gtsam::GaussianConditional* eliminate(size_t nrFrontals);
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gtsam::GaussianFactor* negate() const;
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Matrix computeInformation() const;
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void transposeMultiplyAdd(double alpha, const Vector& e, gtsam::VectorValues& x) const;
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gtsam::JacobianFactor whiten() const;
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gtsam::GaussianConditional* eliminateFirst();
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gtsam::GaussianConditional* eliminate(size_t nFrontals);
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gtsam::GaussianConditional* splitConditional(size_t nFrontals);
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void setModel(bool anyConstrained, const Vector& sigmas);
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void assertInvariants() const;
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//gtsam::SharedDiagonal& get_model();
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};
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virtual class HessianFactor : gtsam::GaussianFactor {
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//Constructors
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HessianFactor(const gtsam::HessianFactor& gf);
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HessianFactor();
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HessianFactor(size_t j, Matrix G, Vector g, double f);
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@ -980,12 +1131,26 @@ virtual class HessianFactor : gtsam::GaussianFactor {
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double f);
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HessianFactor(const gtsam::GaussianConditional& cg);
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HessianFactor(const gtsam::GaussianFactor& factor);
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//Testable
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size_t size() const;
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void print(string s) const;
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void printKeys(string s) const;
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bool equals(const gtsam::GaussianFactor& lf, double tol) const;
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double error(const gtsam::VectorValues& c) const;
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//Standard Interface
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size_t rows() const;
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gtsam::GaussianFactor* negate() const;
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Matrix info() const;
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double constantTerm() const;
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Vector linearTerm() const;
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Matrix computeInformation() const;
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//Advanced Interface
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void partialCholesky(size_t nrFrontals);
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gtsam::GaussianConditional* splitEliminatedFactor(size_t nrFrontals);
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void assertInvariants() const;
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};
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class GaussianFactorGraph {
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@ -1003,6 +1168,7 @@ class GaussianFactorGraph {
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// Building the graph
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void push_back(gtsam::GaussianFactor* factor);
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void add(const gtsam::GaussianFactor& factor);
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void add(Vector b);
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void add(size_t key1, Matrix A1, Vector b, const gtsam::noiseModel::Diagonal* model);
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void add(size_t key1, Matrix A1, size_t key2, Matrix A2, Vector b,
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@ -1010,6 +1176,9 @@ class GaussianFactorGraph {
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void add(size_t key1, Matrix A1, size_t key2, Matrix A2, size_t key3, Matrix A3,
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Vector b, const gtsam::noiseModel::Diagonal* model);
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|
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//Permutations
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void permuteWithInverse(const gtsam::Permutation& inversePermutation);
|
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// error and probability
|
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double error(const gtsam::VectorValues& c) const;
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double probPrime(const gtsam::VectorValues& c) const;
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@ -1028,19 +1197,48 @@ class GaussianFactorGraph {
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pair<Matrix,Vector> hessian() const;
|
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};
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//Non-Class functions in GaussianFactorGraph.h
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/*void multiplyInPlace(const gtsam::GaussianFactorGraph& fg, const gtsam::VectorValues& x, gtsam::Errors& e);
|
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gtsam::VectorValues gradient(const gtsam::GaussianFactorGraph& fg, const gtsam::VectorValues& x0);
|
||||
void gradientAtZero(const gtsam::GaussianFactorGraph& fg, gtsam::VectorValues& g);
|
||||
void residual(const gtsam::GaussianFactorGraph& fg, const gtsam::VectorValues& x, gtsam::VectorValues& r);
|
||||
void multiply(const gtsam::GaussianFactorGraph& fg, const gtsam::VectorValues& x, gtsam::VectorValues& r);
|
||||
void transposeMultiply(const gtsam::GaussianFactorGraph& fg, const gtsam::VectorValues& x, gtsam::VectorValues& r);*/
|
||||
|
||||
class Errors {
|
||||
//Constructors
|
||||
Errors();
|
||||
Errors(const gtsam::VectorValues& V);
|
||||
|
||||
//Testable
|
||||
void print(string s);
|
||||
bool equals(const gtsam::Errors& expected, double tol) const;
|
||||
};
|
||||
|
||||
//Non-Class functions for Errors
|
||||
//double dot(const gtsam::Errors& A, const gtsam::Errors& b);
|
||||
|
||||
class GaussianISAM {
|
||||
//Constructor
|
||||
GaussianISAM();
|
||||
|
||||
//Standard Interface
|
||||
void saveGraph(string s) const;
|
||||
gtsam::GaussianFactor* marginalFactor(size_t j) const;
|
||||
gtsam::GaussianBayesNet* marginalBayesNet(size_t key) const;
|
||||
Matrix marginalCovariance(size_t key) const;
|
||||
gtsam::GaussianBayesNet* jointBayesNet(size_t key1, size_t key2) const;
|
||||
void clear();
|
||||
};
|
||||
|
||||
#include <gtsam/linear/GaussianSequentialSolver.h>
|
||||
class GaussianSequentialSolver {
|
||||
//Constructors
|
||||
GaussianSequentialSolver(const gtsam::GaussianFactorGraph& graph,
|
||||
bool useQR);
|
||||
|
||||
//Standard Interface
|
||||
void replaceFactors(const gtsam::GaussianFactorGraph* factorGraph);
|
||||
gtsam::GaussianBayesNet* eliminate() const;
|
||||
gtsam::VectorValues* optimize() const;
|
||||
gtsam::GaussianFactor* marginalFactor(size_t j) const;
|
||||
|
|
@ -1053,6 +1251,7 @@ virtual class IterativeOptimizationParameters {
|
|||
string getVerbosity() const;
|
||||
void setKernel(string s) ;
|
||||
void setVerbosity(string s) ;
|
||||
void print() const;
|
||||
};
|
||||
|
||||
//virtual class IterativeSolver {
|
||||
|
|
@ -1074,6 +1273,7 @@ virtual class ConjugateGradientParameters : gtsam::IterativeOptimizationParamete
|
|||
void setReset(size_t value);
|
||||
void setEpsilon_rel(double value);
|
||||
void setEpsilon_abs(double value);
|
||||
void print(string s);
|
||||
};
|
||||
|
||||
#include <gtsam/linear/SubgraphSolver.h>
|
||||
|
|
@ -1084,6 +1284,7 @@ virtual class SubgraphSolverParameters : gtsam::ConjugateGradientParameters {
|
|||
|
||||
class SubgraphSolver {
|
||||
SubgraphSolver(const gtsam::GaussianFactorGraph &A, const gtsam::SubgraphSolverParameters ¶meters);
|
||||
SubgraphSolver(const gtsam::GaussianFactorGraph &Ab1, const gtsam::GaussianFactorGraph &Ab2, const gtsam::SubgraphSolverParameters ¶meters);
|
||||
gtsam::VectorValues optimize() const;
|
||||
};
|
||||
|
||||
|
|
@ -1194,27 +1395,29 @@ class Values {
|
|||
|
||||
size_t size() const;
|
||||
bool empty() const;
|
||||
void clear();
|
||||
size_t dim() const;
|
||||
void clear();
|
||||
size_t dim() const;
|
||||
|
||||
void print(string s) const;
|
||||
bool equals(const gtsam::Values& other, double tol) const;
|
||||
|
||||
void insert(size_t j, const gtsam::Value& value);
|
||||
void insert(const gtsam::Values& values);
|
||||
void update(size_t j, const gtsam::Value& val);
|
||||
void update(const gtsam::Values& values);
|
||||
void erase(size_t j);
|
||||
void insert(const gtsam::Values& values);
|
||||
void update(size_t j, const gtsam::Value& val);
|
||||
void update(const gtsam::Values& values);
|
||||
void erase(size_t j);
|
||||
void swap(gtsam::Values& values);
|
||||
|
||||
bool exists(size_t j) const;
|
||||
gtsam::Value at(size_t j) const;
|
||||
gtsam::KeyList keys() const;
|
||||
|
||||
gtsam::VectorValues zeroVectors(const gtsam::Ordering& ordering) const;
|
||||
gtsam::VectorValues zeroVectors(const gtsam::Ordering& ordering) const;
|
||||
gtsam::Ordering* orderingArbitrary(size_t firstVar) const;
|
||||
|
||||
gtsam::Values retract(const gtsam::VectorValues& delta, const gtsam::Ordering& ordering) const;
|
||||
gtsam::VectorValues localCoordinates(const gtsam::Values& cp, const gtsam::Ordering& ordering) const;
|
||||
void localCoordinates(const gtsam::Values& cp, const gtsam::Ordering& ordering, gtsam::VectorValues& delta) const;
|
||||
gtsam::Values retract(const gtsam::VectorValues& delta, const gtsam::Ordering& ordering) const;
|
||||
gtsam::VectorValues localCoordinates(const gtsam::Values& cp, const gtsam::Ordering& ordering) const;
|
||||
void localCoordinates(const gtsam::Values& cp, const gtsam::Ordering& ordering, gtsam::VectorValues& delta) const;
|
||||
};
|
||||
|
||||
// Actually a FastList<Key>
|
||||
|
|
@ -1493,7 +1696,7 @@ class ISAM2 {
|
|||
gtsam::ISAM2Result update(const gtsam::NonlinearFactorGraph& newFactors, const gtsam::Values& newTheta);
|
||||
gtsam::ISAM2Result update(const gtsam::NonlinearFactorGraph& newFactors, const gtsam::Values& newTheta, const gtsam::KeyVector& removeFactorIndices);
|
||||
// TODO: wrap the full version of update
|
||||
//void update(const gtsam::NonlinearFactorGraph& newFactors, const gtsam::Values& newTheta, const gtsam::KeyVector& removeFactorIndices, FastMap<Key,int>& constrainedKeys);
|
||||
//void update(const gtsam::NonlinearFactorGraph& newFactors, const gtsam::Values& newTheta, const gtsam::KeyVector& removeFactorIndices, FastMap<Key,int>& constrainedKeys);
|
||||
//void update(const gtsam::NonlinearFactorGraph& newFactors, const gtsam::Values& newTheta, const gtsam::KeyVector& removeFactorIndices, FastMap<Key,int>& constrainedKeys, bool force_relinearize);
|
||||
|
||||
gtsam::Values getLinearizationPoint() const;
|
||||
|
|
|
|||
Loading…
Reference in New Issue