265 lines
11 KiB
C++
265 lines
11 KiB
C++
/* ----------------------------------------------------------------------------
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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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//if (k2pi_noise - 2*k*M_PI > 1e-5) std::cout << k2pi_noise - 2*k*M_PI << std::endl; // for debug
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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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GaussNewtonParams params;
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params.setMaxIterations(1);
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GaussNewtonOptimizer pose2optimizer(pose2Graph, initialGuessLago, params);
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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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