split .h and .cpp for LagoInitializer
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file LagoInitializer.h
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* @author Luca Carlone
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* @author Frank Dellaert
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* @date May 14, 2014
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*/
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#include <gtsam/nonlinear/LagoInitializer.h>
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namespace gtsam {
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/* ************************************************************************* */
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double computeThetaToRoot(const Key nodeKey, const PredecessorMap<Key>& tree,
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const key2doubleMap& deltaThetaMap, const key2doubleMap& thetaFromRootMap) {
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double nodeTheta = 0;
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Key key_child = nodeKey; // the node
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Key key_parent = 0; // the initialization does not matter
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while(1){
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// We check if we reached the root
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if(tree.at(key_child)==key_child) // if we reached the root
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break;
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// we sum the delta theta corresponding to the edge parent->child
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nodeTheta += deltaThetaMap.at(key_child);
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// we get the parent
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key_parent = tree.at(key_child); // the parent
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// we check if we connected to some part of the tree we know
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if(thetaFromRootMap.find(key_parent)!=thetaFromRootMap.end()){
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nodeTheta += thetaFromRootMap.at(key_parent);
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break;
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}
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key_child = key_parent; // we move upwards in the tree
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}
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return nodeTheta;
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}
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/* ************************************************************************* */
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key2doubleMap computeThetasToRoot(const key2doubleMap& deltaThetaMap,
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const PredecessorMap<Key>& tree) {
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key2doubleMap thetaToRootMap;
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key2doubleMap::const_iterator it;
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// for all nodes in the tree
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for(it = deltaThetaMap.begin(); it != deltaThetaMap.end(); ++it ){
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// compute the orientation wrt root
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Key nodeKey = it->first;
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double nodeTheta = computeThetaToRoot(nodeKey, tree, deltaThetaMap,
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thetaToRootMap);
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thetaToRootMap.insert(std::pair<Key, double>(nodeKey, nodeTheta));
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}
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return thetaToRootMap;
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}
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/* ************************************************************************* */
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void getSymbolicGraph(
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/*OUTPUTS*/ std::vector<size_t>& spanningTreeIds, std::vector<size_t>& chordsIds, key2doubleMap& deltaThetaMap,
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/*INPUTS*/ const PredecessorMap<Key>& tree, const NonlinearFactorGraph& g){
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// Get keys for which you want the orientation
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size_t id=0;
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// Loop over the factors
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BOOST_FOREACH(const boost::shared_ptr<NonlinearFactor>& factor, g){
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if (factor->keys().size() == 2){
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Key key1 = factor->keys()[0];
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Key key2 = factor->keys()[1];
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// recast to a between
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boost::shared_ptr< BetweenFactor<Pose2> > pose2Between =
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boost::dynamic_pointer_cast< BetweenFactor<Pose2> >(factor);
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if (!pose2Between) continue;
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// get the orientation - measured().theta();
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double deltaTheta = pose2Between->measured().theta();
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// insert (directed) orientations in the map "deltaThetaMap"
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bool inTree=false;
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if(tree.at(key1)==key2){
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deltaThetaMap.insert(std::pair<Key, double>(key1, -deltaTheta));
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inTree = true;
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} else if(tree.at(key2)==key1){
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deltaThetaMap.insert(std::pair<Key, double>(key2, deltaTheta));
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inTree = true;
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}
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// store factor slot, distinguishing spanning tree edges from chordsIds
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if(inTree == true)
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spanningTreeIds.push_back(id);
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else // it's a chord!
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chordsIds.push_back(id);
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}
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id++;
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}
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}
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/* ************************************************************************* */
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void getDeltaThetaAndNoise(NonlinearFactor::shared_ptr factor,
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Vector& deltaTheta, noiseModel::Diagonal::shared_ptr& model_deltaTheta) {
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// Get the relative rotation measurement from the between factor
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boost::shared_ptr<BetweenFactor<Pose2> > pose2Between =
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boost::dynamic_pointer_cast<BetweenFactor<Pose2> >(factor);
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if (!pose2Between)
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throw std::invalid_argument("buildLinearOrientationGraph: invalid between factor!");
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deltaTheta = (Vector(1) << pose2Between->measured().theta());
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// Retrieve the noise model for the relative rotation
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SharedNoiseModel model = pose2Between->get_noiseModel();
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boost::shared_ptr<noiseModel::Diagonal> diagonalModel =
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boost::dynamic_pointer_cast<noiseModel::Diagonal>(model);
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if (!diagonalModel)
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throw std::invalid_argument("buildLinearOrientationGraph: invalid noise model "
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"(current version assumes diagonal noise model)!");
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Vector std_deltaTheta = (Vector(1) << diagonalModel->sigma(2) ); // std on the angular measurement
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model_deltaTheta = noiseModel::Diagonal::Sigmas(std_deltaTheta);
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}
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/* ************************************************************************* */
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GaussianFactorGraph buildLinearOrientationGraph(const std::vector<size_t>& spanningTreeIds, const std::vector<size_t>& chordsIds,
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const NonlinearFactorGraph& g, const key2doubleMap& orientationsToRoot, const PredecessorMap<Key>& tree){
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GaussianFactorGraph lagoGraph;
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Vector deltaTheta;
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noiseModel::Diagonal::shared_ptr model_deltaTheta;
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Matrix I = eye(1);
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// put original measurements in the spanning tree
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BOOST_FOREACH(const size_t& factorId, spanningTreeIds){
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const FastVector<Key>& keys = g[factorId]->keys();
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Key key1 = keys[0], key2 = keys[1];
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getDeltaThetaAndNoise(g[factorId], deltaTheta, model_deltaTheta);
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lagoGraph.add(JacobianFactor(key1, -I, key2, I, deltaTheta, model_deltaTheta));
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}
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// put regularized measurements in the chordsIds
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BOOST_FOREACH(const size_t& factorId, chordsIds){
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const FastVector<Key>& keys = g[factorId]->keys();
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Key key1 = keys[0], key2 = keys[1];
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getDeltaThetaAndNoise(g[factorId], deltaTheta, model_deltaTheta);
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double key1_DeltaTheta_key2 = deltaTheta(0);
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double k2pi_noise = key1_DeltaTheta_key2 + orientationsToRoot.at(key1) - orientationsToRoot.at(key2); // this coincides to summing up measurements along the cycle induced by the chord
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double k = round(k2pi_noise/(2*M_PI));
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Vector deltaThetaRegularized = (Vector(1) << key1_DeltaTheta_key2 - 2*k*M_PI);
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lagoGraph.add(JacobianFactor(key1, -I, key2, I, deltaThetaRegularized, model_deltaTheta));
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}
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// prior on the anchor orientation
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lagoGraph.add(JacobianFactor(keyAnchor, I, (Vector(1) << 0.0), priorOrientationNoise));
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return lagoGraph;
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}
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/* ************************************************************************* */
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NonlinearFactorGraph buildPose2graph(const NonlinearFactorGraph& graph){
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NonlinearFactorGraph pose2Graph;
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BOOST_FOREACH(const boost::shared_ptr<NonlinearFactor>& factor, graph){
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// recast to a between on Pose2
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boost::shared_ptr< BetweenFactor<Pose2> > pose2Between =
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boost::dynamic_pointer_cast< BetweenFactor<Pose2> >(factor);
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if (pose2Between)
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pose2Graph.add(pose2Between);
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// recast PriorFactor<Pose2> to BetweenFactor<Pose2>
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boost::shared_ptr< PriorFactor<Pose2> > pose2Prior =
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boost::dynamic_pointer_cast< PriorFactor<Pose2> >(factor);
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if (pose2Prior)
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pose2Graph.add(BetweenFactor<Pose2>(keyAnchor, pose2Prior->keys()[0],
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pose2Prior->prior(), pose2Prior->get_noiseModel()));
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}
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return pose2Graph;
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}
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/* ************************************************************************* */
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VectorValues computeLagoOrientations(const NonlinearFactorGraph& pose2Graph){
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// Find a minimum spanning tree
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PredecessorMap<Key> tree = findMinimumSpanningTree<NonlinearFactorGraph, Key, BetweenFactor<Pose2> >(pose2Graph);
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// Create a linear factor graph (LFG) of scalars
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key2doubleMap deltaThetaMap;
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std::vector<size_t> spanningTreeIds; // ids of between factors forming the spanning tree T
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std::vector<size_t> chordsIds; // ids of between factors corresponding to chordsIds wrt T
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getSymbolicGraph(spanningTreeIds, chordsIds, deltaThetaMap, tree, pose2Graph);
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// temporary structure to correct wraparounds along loops
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key2doubleMap orientationsToRoot = computeThetasToRoot(deltaThetaMap, tree);
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// regularize measurements and plug everything in a factor graph
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GaussianFactorGraph lagoGraph = buildLinearOrientationGraph(spanningTreeIds, chordsIds, pose2Graph, orientationsToRoot, tree);
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// Solve the LFG
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VectorValues orientationsLago = lagoGraph.optimize();
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return orientationsLago;
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}
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/* ************************************************************************* */
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VectorValues initializeOrientationsLago(const NonlinearFactorGraph& graph) {
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// We "extract" the Pose2 subgraph of the original graph: this
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// is done to properly model priors and avoiding operating on a larger graph
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NonlinearFactorGraph pose2Graph = buildPose2graph(graph);
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// Get orientations from relative orientation measurements
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return computeLagoOrientations(pose2Graph);
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}
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/* ************************************************************************* */
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Values initializeLago(const NonlinearFactorGraph& graph) {
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// We "extract" the Pose2 subgraph of the original graph: this
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// is done to properly model priors and avoiding operating on a larger graph
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NonlinearFactorGraph pose2Graph = buildPose2graph(graph);
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// Get orientations from relative orientation measurements
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VectorValues orientationsLago = computeLagoOrientations(pose2Graph);
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Values initialGuessLago;
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// for all nodes in the tree
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for(VectorValues::const_iterator it = orientationsLago.begin(); it != orientationsLago.end(); ++it ){
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Key key = it->first;
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Vector orientation = orientationsLago.at(key);
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Pose2 poseLago = Pose2(0.0,0.0,orientation(0));
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initialGuessLago.insert(key, poseLago);
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}
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// add prior needed by GN
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pose2Graph.add(PriorFactor<Pose2>(keyAnchor, Pose2(), priorPose2Noise));
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// Optimize Pose2, with initialGuessLago as initial guess
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GaussNewtonOptimizer pose2optimizer(pose2Graph, initialGuessLago);
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initialGuessLago = pose2optimizer.optimize();
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initialGuessLago.erase(keyAnchor); // that was fictitious
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return initialGuessLago;
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}
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/* ************************************************************************* */
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Values initializeLago(const NonlinearFactorGraph& graph, const Values& initialGuess) {
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Values initialGuessLago;
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// get the orientation estimates from LAGO
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VectorValues orientations = initializeOrientationsLago(graph);
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// for all nodes in the tree
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for(VectorValues::const_iterator it = orientations.begin(); it != orientations.end(); ++it ){
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Key key = it->first;
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if (key != keyAnchor){
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Pose2 pose = initialGuess.at<Pose2>(key);
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Vector orientation = orientations.at(key);
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Pose2 poseLago = Pose2(pose.x(),pose.y(),orientation(0));
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initialGuessLago.insert(key, poseLago);
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}
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}
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return initialGuessLago;
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}
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} // end of namespace gtsam
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@ -10,7 +10,7 @@
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* -------------------------------------------------------------------------- */
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* -------------------------------------------------------------------------- */
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/**
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/**
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* @file testPlanarSLAMExample_lago.cpp
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* @file LagoInitializer.h
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* @brief Initialize Pose2 in a factor graph using LAGO
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* @brief Initialize Pose2 in a factor graph using LAGO
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* (Linear Approximation for Graph Optimization). see papers:
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* (Linear Approximation for Graph Optimization). see papers:
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*
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*
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@ -48,271 +48,50 @@ const Key keyAnchor = symbol('Z',9999999);
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noiseModel::Diagonal::shared_ptr priorOrientationNoise = noiseModel::Diagonal::Variances((Vector(1) << 1e-8));
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noiseModel::Diagonal::shared_ptr priorOrientationNoise = noiseModel::Diagonal::Variances((Vector(1) << 1e-8));
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noiseModel::Diagonal::shared_ptr priorPose2Noise = noiseModel::Diagonal::Variances((Vector(3) << 1e-6, 1e-6, 1e-8));
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noiseModel::Diagonal::shared_ptr priorPose2Noise = noiseModel::Diagonal::Variances((Vector(3) << 1e-6, 1e-6, 1e-8));
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/*
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/* This function computes the cumulative orientation (without wrapping) wrt the root of a spanning tree (tree)
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* This function computes the cumulative orientation (without wrapping) wrt the root of a spanning tree (tree)
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* for a node (nodeKey). The function starts at the nodes and moves towards the root
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* for a node (nodeKey). The function starts at the nodes and moves towards the root
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* summing up the (directed) rotation measurements. Relative measurements are encoded in "deltaThetaMap"
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* summing up the (directed) rotation measurements. Relative measurements are encoded in "deltaThetaMap"
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* The root is assumed to have orientation zero.
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* The root is assumed to have orientation zero.
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*/
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*/
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double computeThetaToRoot(const Key nodeKey, const PredecessorMap<Key>& tree,
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double computeThetaToRoot(const Key nodeKey, const PredecessorMap<Key>& tree,
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const key2doubleMap& deltaThetaMap, const key2doubleMap& thetaFromRootMap) {
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const key2doubleMap& deltaThetaMap, const key2doubleMap& thetaFromRootMap);
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double nodeTheta = 0;
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/* This function computes the cumulative orientations (without wrapping)
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Key key_child = nodeKey; // the node
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Key key_parent = 0; // the initialization does not matter
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while(1){
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// We check if we reached the root
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if(tree.at(key_child)==key_child) // if we reached the root
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break;
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// we sum the delta theta corresponding to the edge parent->child
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nodeTheta += deltaThetaMap.at(key_child);
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// we get the parent
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key_parent = tree.at(key_child); // the parent
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// we check if we connected to some part of the tree we know
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if(thetaFromRootMap.find(key_parent)!=thetaFromRootMap.end()){
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nodeTheta += thetaFromRootMap.at(key_parent);
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break;
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}
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key_child = key_parent; // we move upwards in the tree
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}
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return nodeTheta;
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}
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/*
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* This function computes the cumulative orientations (without wrapping)
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* for all node wrt the root (root has zero orientation)
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* for all node wrt the root (root has zero orientation)
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*/
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*/
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key2doubleMap computeThetasToRoot(const key2doubleMap& deltaThetaMap,
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key2doubleMap computeThetasToRoot(const key2doubleMap& deltaThetaMap,
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const PredecessorMap<Key>& tree) {
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const PredecessorMap<Key>& tree);
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key2doubleMap thetaToRootMap;
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/* Given a factor graph "g", and a spanning tree "tree", the function selects the nodes belonging to the tree and to g,
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key2doubleMap::const_iterator it;
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// for all nodes in the tree
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for(it = deltaThetaMap.begin(); it != deltaThetaMap.end(); ++it )
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{
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// compute the orientation wrt root
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Key nodeKey = it->first;
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double nodeTheta = computeThetaToRoot(nodeKey, tree, deltaThetaMap,
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thetaToRootMap);
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thetaToRootMap.insert(std::pair<Key, double>(nodeKey, nodeTheta));
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}
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return thetaToRootMap;
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}
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/*
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* Given a factor graph "g", and a spanning tree "tree", the function selects the nodes belonging to the tree and to g,
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* and stores the factor slots corresponding to edges in the tree and to chordsIds wrt this tree
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* and stores the factor slots corresponding to edges in the tree and to chordsIds wrt this tree
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* Also it computes deltaThetaMap which is a fast way to encode relative orientations along the tree:
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* Also it computes deltaThetaMap which is a fast way to encode relative orientations along the tree:
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* for a node key2, s.t. tree[key2]=key1, the values deltaThetaMap[key2] is the relative orientation theta[key2]-theta[key1]
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* for a node key2, s.t. tree[key2]=key1, the values deltaThetaMap[key2] is the relative orientation theta[key2]-theta[key1]
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*/
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*/
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void getSymbolicGraph(
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void getSymbolicGraph(
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/*OUTPUTS*/ std::vector<size_t>& spanningTreeIds, std::vector<size_t>& chordsIds, key2doubleMap& deltaThetaMap,
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/*OUTPUTS*/ std::vector<size_t>& spanningTreeIds, std::vector<size_t>& chordsIds, key2doubleMap& deltaThetaMap,
|
||||||
/*INPUTS*/ const PredecessorMap<Key>& tree, const NonlinearFactorGraph& g){
|
/*INPUTS*/ const PredecessorMap<Key>& tree, const NonlinearFactorGraph& g);
|
||||||
|
|
||||||
// Get keys for which you want the orientation
|
/* Retrieves the deltaTheta and the corresponding noise model from a BetweenFactor<Pose2> */
|
||||||
size_t id=0;
|
|
||||||
// Loop over the factors
|
|
||||||
BOOST_FOREACH(const boost::shared_ptr<NonlinearFactor>& factor, g){
|
|
||||||
if (factor->keys().size() == 2){
|
|
||||||
Key key1 = factor->keys()[0];
|
|
||||||
Key key2 = factor->keys()[1];
|
|
||||||
|
|
||||||
// recast to a between
|
|
||||||
boost::shared_ptr< BetweenFactor<Pose2> > pose2Between =
|
|
||||||
boost::dynamic_pointer_cast< BetweenFactor<Pose2> >(factor);
|
|
||||||
if (!pose2Between) continue;
|
|
||||||
|
|
||||||
// get the orientation - measured().theta();
|
|
||||||
double deltaTheta = pose2Between->measured().theta();
|
|
||||||
|
|
||||||
// insert (directed) orientations in the map "deltaThetaMap"
|
|
||||||
bool inTree=false;
|
|
||||||
if(tree.at(key1)==key2){
|
|
||||||
deltaThetaMap.insert(std::pair<Key, double>(key1, -deltaTheta));
|
|
||||||
inTree = true;
|
|
||||||
} else if(tree.at(key2)==key1){
|
|
||||||
deltaThetaMap.insert(std::pair<Key, double>(key2, deltaTheta));
|
|
||||||
inTree = true;
|
|
||||||
}
|
|
||||||
|
|
||||||
// store factor slot, distinguishing spanning tree edges from chordsIds
|
|
||||||
if(inTree == true)
|
|
||||||
spanningTreeIds.push_back(id);
|
|
||||||
else // it's a chord!
|
|
||||||
chordsIds.push_back(id);
|
|
||||||
}
|
|
||||||
id++;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
/*
|
|
||||||
* Retrieves the deltaTheta and the corresponding noise model from a BetweenFactor<Pose2>
|
|
||||||
*/
|
|
||||||
void getDeltaThetaAndNoise(NonlinearFactor::shared_ptr factor,
|
void getDeltaThetaAndNoise(NonlinearFactor::shared_ptr factor,
|
||||||
Vector& deltaTheta, noiseModel::Diagonal::shared_ptr& model_deltaTheta) {
|
Vector& deltaTheta, noiseModel::Diagonal::shared_ptr& model_deltaTheta);
|
||||||
|
|
||||||
// Get the relative rotation measurement from the between factor
|
/* Linear factor graph with regularized orientation measurements */
|
||||||
boost::shared_ptr<BetweenFactor<Pose2> > pose2Between =
|
|
||||||
boost::dynamic_pointer_cast<BetweenFactor<Pose2> >(factor);
|
|
||||||
if (!pose2Between)
|
|
||||||
throw std::invalid_argument("buildLinearOrientationGraph: invalid between factor!");
|
|
||||||
deltaTheta = (Vector(1) << pose2Between->measured().theta());
|
|
||||||
|
|
||||||
// Retrieve the noise model for the relative rotation
|
|
||||||
SharedNoiseModel model = pose2Between->get_noiseModel();
|
|
||||||
boost::shared_ptr<noiseModel::Diagonal> diagonalModel =
|
|
||||||
boost::dynamic_pointer_cast<noiseModel::Diagonal>(model);
|
|
||||||
if (!diagonalModel)
|
|
||||||
throw std::invalid_argument("buildLinearOrientationGraph: invalid noise model "
|
|
||||||
"(current version assumes diagonal noise model)!");
|
|
||||||
Vector std_deltaTheta = (Vector(1) << diagonalModel->sigma(2) ); // std on the angular measurement
|
|
||||||
model_deltaTheta = noiseModel::Diagonal::Sigmas(std_deltaTheta);
|
|
||||||
}
|
|
||||||
|
|
||||||
/*
|
|
||||||
* Linear factor graph with regularized orientation measurements
|
|
||||||
*/
|
|
||||||
GaussianFactorGraph buildLinearOrientationGraph(const std::vector<size_t>& spanningTreeIds, const std::vector<size_t>& chordsIds,
|
GaussianFactorGraph buildLinearOrientationGraph(const std::vector<size_t>& spanningTreeIds, const std::vector<size_t>& chordsIds,
|
||||||
const NonlinearFactorGraph& g, const key2doubleMap& orientationsToRoot, const PredecessorMap<Key>& tree){
|
const NonlinearFactorGraph& g, const key2doubleMap& orientationsToRoot, const PredecessorMap<Key>& tree);
|
||||||
|
|
||||||
GaussianFactorGraph lagoGraph;
|
/* Selects the subgraph of betweenFactors and transforms priors into between wrt a fictitious node */
|
||||||
Vector deltaTheta;
|
NonlinearFactorGraph buildPose2graph(const NonlinearFactorGraph& graph);
|
||||||
noiseModel::Diagonal::shared_ptr model_deltaTheta;
|
|
||||||
|
|
||||||
Matrix I = eye(1);
|
/* Returns the orientations of a graph including only BetweenFactors<Pose2> */
|
||||||
// put original measurements in the spanning tree
|
VectorValues computeLagoOrientations(const NonlinearFactorGraph& pose2Graph);
|
||||||
BOOST_FOREACH(const size_t& factorId, spanningTreeIds){
|
|
||||||
const FastVector<Key>& keys = g[factorId]->keys();
|
|
||||||
Key key1 = keys[0], key2 = keys[1];
|
|
||||||
getDeltaThetaAndNoise(g[factorId], deltaTheta, model_deltaTheta);
|
|
||||||
lagoGraph.add(JacobianFactor(key1, -I, key2, I, deltaTheta, model_deltaTheta));
|
|
||||||
}
|
|
||||||
// put regularized measurements in the chordsIds
|
|
||||||
BOOST_FOREACH(const size_t& factorId, chordsIds){
|
|
||||||
const FastVector<Key>& keys = g[factorId]->keys();
|
|
||||||
Key key1 = keys[0], key2 = keys[1];
|
|
||||||
getDeltaThetaAndNoise(g[factorId], deltaTheta, model_deltaTheta);
|
|
||||||
double key1_DeltaTheta_key2 = deltaTheta(0);
|
|
||||||
double k2pi_noise = key1_DeltaTheta_key2 + orientationsToRoot.at(key1) - orientationsToRoot.at(key2); // this coincides to summing up measurements along the cycle induced by the chord
|
|
||||||
double k = round(k2pi_noise/(2*M_PI));
|
|
||||||
Vector deltaThetaRegularized = (Vector(1) << key1_DeltaTheta_key2 - 2*k*M_PI);
|
|
||||||
lagoGraph.add(JacobianFactor(key1, -I, key2, I, deltaThetaRegularized, model_deltaTheta));
|
|
||||||
}
|
|
||||||
// prior on the anchor orientation
|
|
||||||
lagoGraph.add(JacobianFactor(keyAnchor, I, (Vector(1) << 0.0), priorOrientationNoise));
|
|
||||||
return lagoGraph;
|
|
||||||
}
|
|
||||||
|
|
||||||
/*
|
/* LAGO: Returns the orientations of the Pose2 in a generic factor graph */
|
||||||
* Selects the subgraph of betweenFactors and transforms priors into between wrt a fictitious node
|
VectorValues initializeOrientationsLago(const NonlinearFactorGraph& graph);
|
||||||
*/
|
|
||||||
NonlinearFactorGraph buildPose2graph(const NonlinearFactorGraph& graph){
|
|
||||||
NonlinearFactorGraph pose2Graph;
|
|
||||||
|
|
||||||
BOOST_FOREACH(const boost::shared_ptr<NonlinearFactor>& factor, graph){
|
/* Returns the values for the Pose2 in a generic factor graph */
|
||||||
|
Values initializeLago(const NonlinearFactorGraph& graph);
|
||||||
|
|
||||||
// recast to a between on Pose2
|
/* Only corrects the orientation part in initialGuess */
|
||||||
boost::shared_ptr< BetweenFactor<Pose2> > pose2Between =
|
Values initializeLago(const NonlinearFactorGraph& graph, const Values& initialGuess);
|
||||||
boost::dynamic_pointer_cast< BetweenFactor<Pose2> >(factor);
|
|
||||||
if (pose2Between)
|
|
||||||
pose2Graph.add(pose2Between);
|
|
||||||
|
|
||||||
// recast PriorFactor<Pose2> to BetweenFactor<Pose2>
|
|
||||||
boost::shared_ptr< PriorFactor<Pose2> > pose2Prior =
|
|
||||||
boost::dynamic_pointer_cast< PriorFactor<Pose2> >(factor);
|
|
||||||
if (pose2Prior)
|
|
||||||
pose2Graph.add(BetweenFactor<Pose2>(keyAnchor, pose2Prior->keys()[0],
|
|
||||||
pose2Prior->prior(), pose2Prior->get_noiseModel()));
|
|
||||||
}
|
|
||||||
return pose2Graph;
|
|
||||||
}
|
|
||||||
|
|
||||||
/*
|
|
||||||
* Returns the orientations of a graph including only BetweenFactors<Pose2>
|
|
||||||
*/
|
|
||||||
VectorValues computeLagoOrientations(const NonlinearFactorGraph& pose2Graph){
|
|
||||||
|
|
||||||
// Find a minimum spanning tree
|
|
||||||
PredecessorMap<Key> tree = findMinimumSpanningTree<NonlinearFactorGraph, Key, BetweenFactor<Pose2> >(pose2Graph);
|
|
||||||
|
|
||||||
// Create a linear factor graph (LFG) of scalars
|
|
||||||
key2doubleMap deltaThetaMap;
|
|
||||||
std::vector<size_t> spanningTreeIds; // ids of between factors forming the spanning tree T
|
|
||||||
std::vector<size_t> chordsIds; // ids of between factors corresponding to chordsIds wrt T
|
|
||||||
getSymbolicGraph(spanningTreeIds, chordsIds, deltaThetaMap, tree, pose2Graph);
|
|
||||||
|
|
||||||
// temporary structure to correct wraparounds along loops
|
|
||||||
key2doubleMap orientationsToRoot = computeThetasToRoot(deltaThetaMap, tree);
|
|
||||||
|
|
||||||
// regularize measurements and plug everything in a factor graph
|
|
||||||
GaussianFactorGraph lagoGraph = buildLinearOrientationGraph(spanningTreeIds, chordsIds, pose2Graph, orientationsToRoot, tree);
|
|
||||||
|
|
||||||
// Solve the LFG
|
|
||||||
VectorValues orientationsLago = lagoGraph.optimize();
|
|
||||||
|
|
||||||
return orientationsLago;
|
|
||||||
}
|
|
||||||
|
|
||||||
/*
|
|
||||||
* Returns the orientations of the Pose2 in a generic factor graph
|
|
||||||
*/
|
|
||||||
VectorValues initializeOrientationsLago(const NonlinearFactorGraph& graph) {
|
|
||||||
|
|
||||||
// We "extract" the Pose2 subgraph of the original graph: this
|
|
||||||
// is done to properly model priors and avoiding operating on a larger graph
|
|
||||||
NonlinearFactorGraph pose2Graph = buildPose2graph(graph);
|
|
||||||
|
|
||||||
// Get orientations from relative orientation measurements
|
|
||||||
return computeLagoOrientations(pose2Graph);
|
|
||||||
}
|
|
||||||
|
|
||||||
/*
|
|
||||||
* Returns the values for the Pose2 in a generic factor graph
|
|
||||||
*/
|
|
||||||
Values initializeLago(const NonlinearFactorGraph& graph) {
|
|
||||||
|
|
||||||
// We "extract" the Pose2 subgraph of the original graph: this
|
|
||||||
// is done to properly model priors and avoiding operating on a larger graph
|
|
||||||
NonlinearFactorGraph pose2Graph = buildPose2graph(graph);
|
|
||||||
|
|
||||||
// Get orientations from relative orientation measurements
|
|
||||||
VectorValues orientationsLago = computeLagoOrientations(pose2Graph);
|
|
||||||
|
|
||||||
Values initialGuessLago;
|
|
||||||
// for all nodes in the tree
|
|
||||||
for(VectorValues::const_iterator it = orientationsLago.begin(); it != orientationsLago.end(); ++it ){
|
|
||||||
Key key = it->first;
|
|
||||||
Vector orientation = orientationsLago.at(key);
|
|
||||||
Pose2 poseLago = Pose2(0.0,0.0,orientation(0));
|
|
||||||
initialGuessLago.insert(key, poseLago);
|
|
||||||
}
|
|
||||||
pose2Graph.add(PriorFactor<Pose2>(keyAnchor, Pose2(), priorPose2Noise));
|
|
||||||
GaussNewtonOptimizer pose2optimizer(pose2Graph, initialGuessLago);
|
|
||||||
initialGuessLago = pose2optimizer.optimize();
|
|
||||||
initialGuessLago.erase(keyAnchor); // that was fictitious
|
|
||||||
return initialGuessLago;
|
|
||||||
}
|
|
||||||
|
|
||||||
/*
|
|
||||||
* Only corrects the orientation part in initialGuess
|
|
||||||
*/
|
|
||||||
Values initializeLago(const NonlinearFactorGraph& graph, const Values& initialGuess) {
|
|
||||||
Values initialGuessLago;
|
|
||||||
|
|
||||||
// get the orientation estimates from LAGO
|
|
||||||
VectorValues orientations = initializeOrientationsLago(graph);
|
|
||||||
|
|
||||||
// for all nodes in the tree
|
|
||||||
for(VectorValues::const_iterator it = orientations.begin(); it != orientations.end(); ++it ){
|
|
||||||
Key key = it->first;
|
|
||||||
if (key != keyAnchor){
|
|
||||||
Pose2 pose = initialGuess.at<Pose2>(key);
|
|
||||||
Vector orientation = orientations.at(key);
|
|
||||||
Pose2 poseLago = Pose2(pose.x(),pose.y(),orientation(0));
|
|
||||||
initialGuessLago.insert(key, poseLago);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
return initialGuessLago;
|
|
||||||
}
|
|
||||||
|
|
||||||
} // end of namespace gtsam
|
} // end of namespace gtsam
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue