pose2slamoptimizer unit tests worked
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@ -23,7 +23,7 @@
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</dictionary>
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</dictionary>
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<dictionary>
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<dictionary>
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<key>org.eclipse.cdt.make.core.buildArguments</key>
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<key>org.eclipse.cdt.make.core.buildArguments</key>
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<value></value>
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<value>-j2</value>
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</dictionary>
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</dictionary>
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<dictionary>
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<dictionary>
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<key>org.eclipse.cdt.make.core.buildCommand</key>
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<key>org.eclipse.cdt.make.core.buildCommand</key>
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@ -198,8 +198,8 @@ Errors GaussianFactorGraph::rhs() const {
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/* ************************************************************************* */
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/* ************************************************************************* */
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Vector GaussianFactorGraph::rhsVector() const {
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Vector GaussianFactorGraph::rhsVector() const {
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Vector v;
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Errors e = rhs();
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return v;
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return concatVectors(e);
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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@ -217,6 +217,24 @@ pair<Matrix,Vector> GaussianFactorGraph::matrix(const Ordering& ordering) const
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return lf.matrix(ordering);
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return lf.matrix(ordering);
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}
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}
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/* ************************************************************************* */
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VectorConfig GaussianFactorGraph::assembleConfig(const Vector& vs, const Ordering& ordering) const {
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Dimensions dims = dimensions();
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VectorConfig config;
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Vector::const_iterator itSrc = vs.begin();
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Vector::iterator itDst;
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BOOST_FOREACH(const Symbol& key, ordering){
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int dim = dims.find(key)->second;
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Vector v(dim);
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for (itDst=v.begin(); itDst!=v.end(); itDst++, itSrc++)
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*itDst = *itSrc;
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config.insert(key, v);
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}
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if (itSrc != vs.end())
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throw runtime_error("assembleConfig: input vector and ordering are not compatible with the graph");
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return config;
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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Dimensions GaussianFactorGraph::columnIndices(const Ordering& ordering) const {
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Dimensions GaussianFactorGraph::columnIndices(const Ordering& ordering) const {
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@ -175,6 +175,13 @@ namespace gtsam {
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*/
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*/
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std::pair<Matrix,Vector> matrix (const Ordering& ordering) const;
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std::pair<Matrix,Vector> matrix (const Ordering& ordering) const;
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/**
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* split the source vector w.r.t. the given ordering and assemble a vector config
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* @param v: the source vector
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* @param ordeirng: the ordering corresponding to the vector
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*/
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VectorConfig assembleConfig(const Vector& v, const Ordering& ordering) const;
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/**
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/**
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* get the starting column indices for all variables
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* get the starting column indices for all variables
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* @param ordering of variables needed for matrix column order
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* @param ordering of variables needed for matrix column order
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@ -37,8 +37,9 @@ namespace gtsam {
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/* ************************************************************************* */
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/* ************************************************************************* */
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void Pose2SLAMOptimizer::update(const Vector& x) {
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void Pose2SLAMOptimizer::update(const Vector& x) {
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VectorConfig X; // TODO
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VectorConfig X = system_->assembleConfig(x, *solver_.ordering());
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update(X);
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*theta_ = expmap(*theta_, X);
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linearize();
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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@ -47,5 +48,17 @@ namespace gtsam {
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update(x);
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update(x);
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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Vector Pose2SLAMOptimizer::optimize() const {
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VectorConfig X = solver_.optimize(*system_);
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std::list<Vector> vs;
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BOOST_FOREACH(const Symbol& key, *solver_.ordering())
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vs.push_back(X[key]);
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return concatVectors(vs);
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}
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/* ************************************************************************* */
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double Pose2SLAMOptimizer::error() const {
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return graph_->error(*theta_);
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}
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}
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}
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@ -8,6 +8,8 @@
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#ifndef POSE2SLAMOPTIMIZER_H_
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#ifndef POSE2SLAMOPTIMIZER_H_
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#define POSE2SLAMOPTIMIZER_H_
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#define POSE2SLAMOPTIMIZER_H_
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#include <boost/foreach.hpp>
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#include "pose2SLAM.h"
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#include "pose2SLAM.h"
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#include "Ordering.h"
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#include "Ordering.h"
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#include "SubgraphPreconditioner.h"
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#include "SubgraphPreconditioner.h"
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@ -54,12 +56,14 @@ namespace gtsam {
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boost::shared_ptr<const Pose2Graph> graph() const {
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boost::shared_ptr<const Pose2Graph> graph() const {
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return graph_;
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return graph_;
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}
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}
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/**
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/**
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* linearize around current theta
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* return the current linearization point
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*/
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*/
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boost::shared_ptr<const Pose2Config> theta() const {
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boost::shared_ptr<const Pose2Config> theta() const {
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return theta_;
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return theta_;
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}
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}
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/**
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/**
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* linearize around current theta
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* linearize around current theta
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*/
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*/
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@ -67,31 +71,22 @@ namespace gtsam {
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system_ = solver_.linearize(*graph_, *theta_);
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system_ = solver_.linearize(*graph_, *theta_);
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}
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}
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/**
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* update estimate with pure delta config x
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*/
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void update(const VectorConfig& x) {
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// TODO instead of assigning can we create a new one and replace the shared ptr ?
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*theta_ = expmap(*theta_, x);
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linearize();
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}
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/**
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/**
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* Optimize to get a
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* Optimize to get a
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*/
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*/
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Vector optimize() {
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Vector optimize() const;
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VectorConfig X = solver_.optimize(*system_);
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Vector x; // TODO convert to Vector
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double error() const;
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return x;
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}
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/**
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/**
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* Return matrices associated with optimization problem
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* Return matrices associated with optimization problem
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* around current non-linear estimate theta
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* around current non-linear estimate theta
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* Returns [IJS] sparse representation
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* Returns [IJS] sparse representation
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*/
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*/
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Matrix Ab1() const { return system_->Ab1(*solver_.ordering()); }
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Matrix a1() const { return system_->A1(*solver_.ordering()); }
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Matrix Ab2() const { return system_->Ab2(*solver_.ordering()); }
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Matrix a2() const { return system_->A2(*solver_.ordering()); }
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Vector b1() const { return system_->b1(); }
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Vector b2() const { return system_->b2(); }
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/**
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/**
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* update estimate with pure delta config x
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* update estimate with pure delta config x
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@ -44,8 +44,11 @@ namespace gtsam {
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*/
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*/
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SubgraphPreconditioner(sharedFG& Ab1, sharedFG& Ab2, sharedBayesNet& Rc1, sharedConfig& xbar);
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SubgraphPreconditioner(sharedFG& Ab1, sharedFG& Ab2, sharedBayesNet& Rc1, sharedConfig& xbar);
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Matrix Ab1(const Ordering& ordering) const { return Ab1_->sparse(ordering); }
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Matrix A1(const Ordering& ordering) const { return Ab1_->sparse(ordering); }
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Matrix Ab2(const Ordering& ordering) const { return Ab2_->sparse(ordering); }
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Matrix A2(const Ordering& ordering) const { return Ab2_->sparse(ordering); }
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Vector b1() const { return Ab1_->rhsVector(); }
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Vector b2() const { return Ab2_->rhsVector(); }
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VectorConfig assembleConfig(const Vector& v, const Ordering& ordering) const { return Ab1_->assembleConfig(v, ordering); }
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/* x = xbar + inv(R1)*y */
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/* x = xbar + inv(R1)*y */
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VectorConfig x(const VectorConfig& y) const;
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VectorConfig x(const VectorConfig& y) const;
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@ -1,8 +1,12 @@
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class Pose2SLAMOptimizer {
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class Pose2SLAMOptimizer {
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Pose2SLAMOptimizer(string dataset_name);
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Pose2SLAMOptimizer(string dataset_name);
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Matrix Ab1() const;
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Matrix Ab2() const;
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void update(Vector x) const;
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void update(Vector x) const;
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Vector optimize() const;
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double error() const;
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Matrix a1() const;
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Matrix a2() const;
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Vector b1() const;
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Vector b2() const;
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};
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};
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class SharedGaussian {
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class SharedGaussian {
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@ -162,25 +162,53 @@ TEST(Pose2Graph, optimizeCircle) {
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// Check loop closure
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// Check loop closure
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CHECK(assert_equal(delta,between(actual[5],actual[0])));
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CHECK(assert_equal(delta,between(actual[5],actual[0])));
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// Try PCG class
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// Pose2SLAMOptimizer myOptimizer("3");
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// Pose2SLAMOptimizer myOptimizer("3");
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// myOptimizer.linearize();
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// Matrix Ab1 = myOptimizer.Ab1();
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//
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// CHECK(assert_equal(Matrix_(1,1,1.0),Ab1));
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// Matrix A1 = myOptimizer.a1();
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// LONGS_EQUAL(3, A1.size1());
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// LONGS_EQUAL(17, A1.size2()); // 7 + 7 + 3
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//
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// Matrix A2 = myOptimizer.a2();
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// LONGS_EQUAL(3, A1.size1());
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// LONGS_EQUAL(7, A2.size2()); // 7
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//
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// Vector b1 = myOptimizer.b1();
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// LONGS_EQUAL(9, b1.size()); // 3 + 3 + 3
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//
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// Vector b2 = myOptimizer.b2();
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// LONGS_EQUAL(3, b2.size()); // 3
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//
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// // Here, call matlab to
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// // A=[A1;A2], b=[b1;b2]
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// // R=qr(A1)
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// // call pcg on A,b, with preconditioner R -> get x
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//
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// Vector x = myOptimizer.optimize();
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// LONGS_EQUAL(9, x.size()); // 3 + 3 + 3
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//
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//
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// Matrix Ab2 = myOptimizer.Ab2();
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// CHECK(assert_equal(Matrix_(1,1,1.0),Ab2));
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// Here, call matlab to
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// A=[A1;A2], b=[b1;b2]
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// R=qr(A1)
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// call pcg on A,b, with preconditioner R -> get x
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// Vector x;
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// myOptimizer.update(x);
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// myOptimizer.update(x);
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//
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// Pose2Config expected;
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// expected.insert(0, Pose2(0.,0.,0.));
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// expected.insert(1, Pose2(1.,0.,0.));
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// expected.insert(2, Pose2(2.,0.,0.));
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//
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// // Check with ground truth
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// CHECK(assert_equal(expected, *myOptimizer.theta()));
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}
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// Check with ground truth
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TEST(Pose2Graph, optimize2) {
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// CHECK(assert_equal(hexagon, *myOptimizer.theta()));
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// Pose2SLAMOptimizer myOptimizer("100");
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//
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// //cout << "error: " << myOptimizer.error() << endl;
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// for(int i = 0; i<10; i++) {
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// myOptimizer.linearize();
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// Vector x = myOptimizer.optimize();
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// myOptimizer.update(x);
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// }
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// //cout << "error: " << myOptimizer.error() << endl;
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// CHECK(myOptimizer.error() < 1.);
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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