105 lines
3.2 KiB
C++
105 lines
3.2 KiB
C++
/**
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* @file ISAM2.h
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* @brief Incremental update functionality (ISAM2) for BayesTree, with fluid relinearization.
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* @author Michael Kaess
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*/
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// \callgraph
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#pragma once
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#include <map>
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#include <list>
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#include <vector>
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#include <boost/serialization/map.hpp>
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#include <boost/serialization/list.hpp>
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#include <stdexcept>
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#include "Testable.h"
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#include "FactorGraph.h"
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#include "NonlinearFactorGraph.h"
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#include "BayesNet.h"
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#include "BayesTree.h"
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#include "Key.h"
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#include "SymbolMap.h"
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namespace gtsam {
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typedef SymbolMap<GaussianFactor::shared_ptr> CachedFactors;
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template<class Conditional, class Config>
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class ISAM2: public BayesTree<Conditional> {
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protected:
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// current linearization point
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Config theta_;
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Config thetaFuture_; // lin point of next iteration
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// for keeping all original nonlinear factors
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NonlinearFactorGraph<Config> nonlinearFactors_;
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// cached intermediate results for restarting computation in the middle
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CachedFactors cached_;
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// the linear solution, an update to the estimate in theta
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VectorConfig delta_;
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VectorConfig deltaMarked_;
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// variables that have been updated, requiring the corresponding factors to be relinearized
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std::list<Symbol> marked_;
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public:
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/** Create an empty Bayes Tree */
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ISAM2();
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/** Create a Bayes Tree from a Bayes Net */
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ISAM2(const NonlinearFactorGraph<Config>& fg, const Ordering& ordering, const Config& config);
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/** Destructor */
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virtual ~ISAM2() {
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}
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typedef typename BayesTree<Conditional>::sharedClique sharedClique;
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typedef typename BayesTree<Conditional>::Cliques Cliques;
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/**
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* ISAM2. (update_internal provides access to list of orphans for drawing purposes)
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*/
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void update_internal(const NonlinearFactorGraph<Config>& newFactors,
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const Config& newTheta, Cliques& orphans,
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double wildfire_threshold, double relinearize_threshold, bool relinearize);
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void update(const NonlinearFactorGraph<Config>& newFactors, const Config& newTheta,
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double wildfire_threshold = 0., double relinearize_threshold = 0., bool relinearize = true);
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// needed to create initial estimates (note that this will be the linearization point in the next step!)
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const Config getLinearizationPoint() const {return thetaFuture_;}
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// estimate based on incomplete delta (threshold!)
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const Config calculateEstimate() const {return expmap(theta_, delta_);}
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// estimate based on full delta (note that this is based on the actual current linearization point)
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const Config calculateBestEstimate() const {return expmap(theta_, optimize2(*this, 0.));}
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const std::list<Symbol>& getMarkedUnsafe() const { return marked_; }
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const NonlinearFactorGraph<Config>& getFactorsUnsafe() const { return nonlinearFactors_; }
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const Config& getThetaUnsafe() const { return theta_; }
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const VectorConfig& getDeltaUnsafe() const { return delta_; }
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size_t lastAffectedVariableCount;
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size_t lastAffectedFactorCount;
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size_t lastAffectedCliqueCount;
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private:
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std::list<int> getAffectedFactors(const std::list<Symbol>& keys) const;
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FactorGraph<GaussianFactor> relinearizeAffectedFactors(const std::set<Symbol>& affectedKeys) const;
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FactorGraph<GaussianFactor> getCachedBoundaryFactors(Cliques& orphans);
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}; // ISAM2
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} /// namespace gtsam
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