228 lines
7.7 KiB
C++
228 lines
7.7 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 BatchFixedLagSmoother.h
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* @brief An LM-based fixed-lag smoother. To the extent possible, this class mimics the iSAM2
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* interface. However, additional parameters, such as the smoother lag and the timestamp associated
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* with each variable are needed.
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*
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* @author Michael Kaess, Stephen Williams
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* @date Oct 14, 2012
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*/
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// \callgraph
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#pragma once
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#include <gtsam_unstable/nonlinear/LinearizedFactor.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <queue>
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namespace gtsam {
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class BatchFixedLagSmoother {
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public:
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/// Typedef for a shared pointer to an Incremental Fixed-Lag Smoother
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typedef boost::shared_ptr<BatchFixedLagSmoother> shared_ptr;
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/// Typedef for a Key-Timestamp map/database
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typedef std::map<Key, double> KeyTimestampMap;
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typedef std::multimap<double, Key> TimestampKeyMap;
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/**
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* Meta information returned about the update
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*/
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// TODO: Think of some more things to put here
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struct Result {
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size_t iterations; ///< The number of optimizer iterations performed
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size_t nonlinearVariables; ///< The number of variables that can be relinearized
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size_t linearVariables; ///< The number of variables that must keep a constant linearization point
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double error; ///< The final factor graph error
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Result() : iterations(0), nonlinearVariables(0), linearVariables(0), error(0) {};
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};
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/** default constructor */
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BatchFixedLagSmoother(const LevenbergMarquardtParams& parameters = LevenbergMarquardtParams(), double smootherLag = 0.0, bool enforceConsistency = false);
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/** destructor */
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virtual ~BatchFixedLagSmoother() { };
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// TODO: Create a better 'print'
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/** Print the factor for debugging and testing (implementing Testable) */
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virtual void print(const std::string& s = "BatchFixedLagSmoother:\n", const KeyFormatter& keyFormatter = DefaultKeyFormatter) const;
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/** Check if two IncrementalFixedLagSmoother Objects are equal */
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virtual bool equals(const BatchFixedLagSmoother& rhs, double tol = 1e-9) const;
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/**
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* Add new factors, updating the solution and relinearizing as needed.
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*/
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Result update(const NonlinearFactorGraph& newFactors = NonlinearFactorGraph(), const Values& newTheta = Values(), const KeyTimestampMap& timestamps = KeyTimestampMap());
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/** Access the current set of timestamps associated with each variable */
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const KeyTimestampMap& getTimestamps() const {
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return keyTimestampMap_;
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}
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/** Access the current linearization point */
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const Values& getLinearizationPoint() const {
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return theta_;
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}
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/** Compute an estimate from the incomplete linear delta computed during the last update.
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* This delta is incomplete because it was not updated below wildfire_threshold. If only
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* a single variable is needed, it is faster to call calculateEstimate(const KEY&).
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*/
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Values calculateEstimate() const {
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//return theta_.retract(delta_, ordering_);
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return theta_;
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}
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/** Compute an estimate for a single variable using its incomplete linear delta computed
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* during the last update. This is faster than calling the no-argument version of
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* calculateEstimate, which operates on all variables.
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* @param key
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* @return
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*/
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template<class VALUE>
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VALUE calculateEstimate(Key key) const {
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//const Index index = ordering_.at(key);
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//const SubVector delta = delta_.at(index);
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//return theta_.at<VALUE>(key).retract(delta);
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return theta_.at<VALUE>(key);
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}
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/** Compute an estimate using a complete delta computed by a full back-substitution.
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*/
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Values calculateBestEstimate() const {
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return calculateEstimate();
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}
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/** Return the final/best estimate of each variable encountered, including those that have been marginalized out previously.
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*/
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Values calculateFinalEstimate() const {
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Values final(thetaFinal_);
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final.insert(theta_);
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return final;
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}
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/** Access the set of nonlinear factors */
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const NonlinearFactorGraph& getFactorsUnsafe() const {
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return factors_;
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}
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/** return the current set of iSAM2 parameters */
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const LevenbergMarquardtParams& params() const {
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return parameters_;
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}
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/** return the current smoother lag */
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double smootherLag() const {
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return smootherLag_;
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}
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/** return the marginal square root information matrix associated with a specific variable */
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Matrix marginalR(Key key) const;
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/** return the marginal information matrix associated with a specific variable */
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Matrix marginalInformation(Key key) const;
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/** return the marginal covariance associated with a specific variable */
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Matrix marginalCovariance(Key key) const;
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protected:
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/** The nonlinear factors **/
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NonlinearFactorGraph factors_;
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/** The current linearization point **/
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Values theta_;
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/** The final estimate of each variable **/
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Values thetaFinal_;
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/** The L-M optimization parameters **/
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LevenbergMarquardtParams parameters_;
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/** The length of the smoother lag. Any variable older than this amount will be marginalized out. */
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double smootherLag_;
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/** A flag indicating if the optimizer should enforce probabilistic consistency by maintaining the
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* linearization point of all variables involved in linearized/marginal factors at the edge of the
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* smoothing window. This idea is from ??? TODO: Look up paper reference **/
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bool enforceConsistency_;
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/** The current timestamp associated with each tracked key */
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TimestampKeyMap timestampKeyMap_;
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KeyTimestampMap keyTimestampMap_;
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/** A typedef defining an Key-Factor mapping **/
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typedef std::map<Key, std::set<Index> > FactorIndex;
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/** A cross-reference structure to allow efficient factor lookups by key **/
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FactorIndex factorIndex_;
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/** The set of keys involved in current linearized factors. These keys should not be relinearized. **/
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//std::set<Key> linearizedKeys_;
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Values linearizedKeys_;
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/** The set of available factor graph slots. These occur because we are constantly deleting factors, leaving holes. **/
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std::queue<size_t> availableSlots_;
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/** Augment the list of factors with a set of new factors */
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void updateFactors(const NonlinearFactorGraph& newFactors);
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/** Remove factors from the list of factors by slot index */
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void removeFactors(const std::set<size_t>& deleteFactors);
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/** Update the Timestamps associated with the keys */
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void updateKeyTimestampMap(const KeyTimestampMap& newTimestamps);
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/** Find the most recent timestamp of the system */
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double getCurrentTimestamp() const;
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/** Find all of the keys associated with timestamps before the provided time */
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std::set<Key> findKeysBefore(double timestamp) const;
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/** Erase any keys associated with timestamps before the provided time */
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void eraseKeys(const std::set<Key>& keys);
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/** Marginalize out selected variables */
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void marginalizeKeys(const std::set<Key>& marginalizableKeys);
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private:
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static void PrintKeySet(const std::set<Key>& keys, const std::string& label);
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static void PrintSymbolicFactor(const GaussianFactor::shared_ptr& factor, const Ordering& ordering);
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static void PrintSymbolicGraph(const GaussianFactorGraph& graph, const Ordering& ordering, const std::string& label);
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static void PrintSymbolicFactor(const NonlinearFactor::shared_ptr& factor);
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static void PrintSymbolicGraph(const NonlinearFactorGraph& graph, const std::string& label);
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}; // BatchFixedLagSmoother
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} /// namespace gtsam
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