102 lines
3.1 KiB
C++
102 lines
3.1 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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* NonlinearISAM-inl.h
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*
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* Created on: 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 <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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#include <boost/foreach.hpp>
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using namespace gtsam;
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/* ************************************************************************* */
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template<class Values>
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void NonlinearISAM<Values>::update(const Factors& newFactors, 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>
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void NonlinearISAM<Values>::reorder_relinearize() {
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// cout << "Reordering, relinearizing..." << endl;
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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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template<class Values>
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Values NonlinearISAM<Values>::estimate() const {
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if(isam_.size() > 0)
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return linPoint_.expmap(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>
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Matrix NonlinearISAM<Values>::marginalCovariance(const Symbol& key) const {
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Matrix covariance; Vector mean;
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boost::tie(mean, covariance) = isam_.marginal(ordering_[key]);
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return covariance;
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}
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