117 lines
3.6 KiB
C++
117 lines
3.6 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 NonlinearISAM-inl.h
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* @date Jan 19, 2010
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* @author Viorela Ila and Richard Roberts
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*/
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#pragma once
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#include <iostream>
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#include <boost/foreach.hpp>
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#include <gtsam/linear/GaussianFactorGraph.h>
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#include <gtsam/inference/ISAM-inl.h>
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#include <gtsam/nonlinear/Ordering.h>
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#include <gtsam/nonlinear/NonlinearISAM.h>
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using namespace std;
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using namespace gtsam;
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/* ************************************************************************* */
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template<class VALUES, class GRAPH>
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void NonlinearISAM<VALUES,GRAPH>::update(const Factors& newFactors,
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const Values& initialValues) {
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if(newFactors.size() > 0) {
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// Reorder and relinearize every reorderInterval updates
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if(reorderInterval_ > 0 && ++reorderCounter_ >= reorderInterval_) {
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reorder_relinearize();
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reorderCounter_ = 0;
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}
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factors_.push_back(newFactors);
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// Linearize new factors and insert them
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// TODO: optimize for whole config?
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linPoint_.insert(initialValues);
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// Augment ordering
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// FIXME: should just loop over new values
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BOOST_FOREACH(const typename Factors::sharedFactor& factor, newFactors)
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BOOST_FOREACH(const Symbol& key, factor->keys())
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ordering_.tryInsert(key, ordering_.nVars()); // will do nothing if already present
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boost::shared_ptr<GaussianFactorGraph> linearizedNewFactors(
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newFactors.linearize(linPoint_, ordering_)->template dynamicCastFactors<GaussianFactorGraph>());
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// Update ISAM
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isam_.update(*linearizedNewFactors);
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}
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}
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/* ************************************************************************* */
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template<class VALUES, class GRAPH>
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void NonlinearISAM<VALUES,GRAPH>::reorder_relinearize() {
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// cout << "Reordering, relinearizing..." << endl;
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if(factors_.size() > 0) {
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// Obtain the new linearization point
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const Values newLinPoint = estimate();
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isam_.clear();
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// Compute an ordering
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ordering_ = *factors_.orderingCOLAMD(newLinPoint);
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// Create a linear factor graph at the new linearization point
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boost::shared_ptr<GaussianFactorGraph> gfg(
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factors_.linearize(newLinPoint, ordering_)->template dynamicCastFactors<GaussianFactorGraph>());
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// Just recreate the whole BayesTree
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isam_.update(*gfg);
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// Update linearization point
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linPoint_ = newLinPoint;
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}
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}
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/* ************************************************************************* */
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template<class VALUES, class GRAPH>
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VALUES NonlinearISAM<VALUES,GRAPH>::estimate() const {
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if(isam_.size() > 0)
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return linPoint_.retract(optimize(isam_), ordering_);
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else
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return linPoint_;
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}
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/* ************************************************************************* */
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template<class VALUES, class GRAPH>
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Matrix NonlinearISAM<VALUES,GRAPH>::marginalCovariance(const Symbol& key) const {
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return isam_.marginalCovariance(ordering_[key]);
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}
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/* ************************************************************************* */
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template<class VALUES, class GRAPH>
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void NonlinearISAM<VALUES,GRAPH>::print(const std::string& s) const {
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cout << "ISAM - " << s << ":" << endl;
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cout << " ReorderInterval: " << reorderInterval_ << " Current Count: " << reorderCounter_ << endl;
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isam_.print("GaussianISAM");
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linPoint_.print("Linearization Point");
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ordering_.print("System Ordering");
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factors_.print("Nonlinear Graph");
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}
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