CMultiMetricMapPDF.h

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2018, Individual contributors, see AUTHORS file     |
   | See: http://www.mrpt.org/Authors - All rights reserved.                |
   | Released under BSD License. See details in http://www.mrpt.org/License |
   +------------------------------------------------------------------------+ */
#ifndef CMultiMetricMapPDF_H
#define CMultiMetricMapPDF_H

#include <mrpt/maps/CMultiMetricMap.h>
#include <mrpt/maps/CSimpleMap.h>
#include <mrpt/poses/CPosePDFParticles.h>
#include <mrpt/poses/CPose3DPDFParticles.h>

#include <mrpt/poses/CPoseRandomSampler.h>

#include <mrpt/bayes/CParticleFilterCapable.h>
#include <mrpt/config/CLoadableOptions.h>
#include <mrpt/slam/CICP.h>

#include <mrpt/slam/PF_implementations_data.h>

namespace mrpt
{
namespace slam
{
class CMetricMapBuilderRBPF;
}
namespace maps
{
/** Auxiliary class used in mrpt::maps::CMultiMetricMapPDF
 * \ingroup mrpt_slam_grp
  */
class CRBPFParticleData : public mrpt::serialization::CSerializable
{
    DEFINE_SERIALIZABLE(CRBPFParticleData)
   public:
    CRBPFParticleData(
        const TSetOfMetricMapInitializers* mapsInitializers = nullptr)
        : mapTillNow(mapsInitializers), robotPath()
    {
    }

    CMultiMetricMap mapTillNow;
    std::deque<mrpt::math::TPose3D> robotPath;
};

/** Declares a class that represents a Rao-Blackwellized set of particles for
 * solving the SLAM problem (This class is the base of RBPF-SLAM applications).
 *   This class is used internally by the map building algorithm in
 * "mrpt::slam::CMetricMapBuilderRBPF"
 *
 * \sa mrpt::slam::CMetricMapBuilderRBPF
 * \ingroup metric_slam_grp
 */
class CMultiMetricMapPDF
    : public mrpt::serialization::CSerializable,
      public mrpt::bayes::CParticleFilterData<CRBPFParticleData>,
      public mrpt::bayes::CParticleFilterDataImpl<
          CMultiMetricMapPDF,
          mrpt::bayes::CParticleFilterData<CRBPFParticleData>::CParticleList>,
      public mrpt::slam::PF_implementation<
          CRBPFParticleData, CMultiMetricMapPDF,
          mrpt::bayes::particle_storage_mode::POINTER>
{
    friend class mrpt::slam::CMetricMapBuilderRBPF;

    DEFINE_SERIALIZABLE(CMultiMetricMapPDF)

   protected:
    // PF algorithm implementations:
    void prediction_and_update_pfStandardProposal(
        const mrpt::obs::CActionCollection* action,
        const mrpt::obs::CSensoryFrame* observation,
        const bayes::CParticleFilter::TParticleFilterOptions& PF_options)
        override;
    void prediction_and_update_pfOptimalProposal(
        const mrpt::obs::CActionCollection* action,
        const mrpt::obs::CSensoryFrame* observation,
        const bayes::CParticleFilter::TParticleFilterOptions& PF_options)
        override;
    void prediction_and_update_pfAuxiliaryPFOptimal(
        const mrpt::obs::CActionCollection* action,
        const mrpt::obs::CSensoryFrame* observation,
        const bayes::CParticleFilter::TParticleFilterOptions& PF_options)
        override;
    void prediction_and_update_pfAuxiliaryPFStandard(
        const mrpt::obs::CActionCollection* action,
        const mrpt::obs::CSensoryFrame* observation,
        const bayes::CParticleFilter::TParticleFilterOptions& PF_options)
        override;

   private:
    /** Internal buffer for the averaged map. */
    mrpt::maps::CMultiMetricMap averageMap;
    bool averageMapIsUpdated;

    /** The SFs and their corresponding pose estimations */
    mrpt::maps::CSimpleMap SFs;
    /** A mapping between indexes in the SFs to indexes in the robot paths from
     * particles. */
    std::vector<uint32_t> SF2robotPath;

   public:
    /** The struct for passing extra simulation parameters to the
     * prediction/update stage
     *    when running a particle filter.
     * \sa prediction_and_update
     */
    struct TPredictionParams : public mrpt::config::CLoadableOptions
    {
        /** Default settings method */
        TPredictionParams();

        void loadFromConfigFile(
            const mrpt::config::CConfigFileBase& source,
            const std::string& section) override;  // See base docs
        void dumpToTextStream(
            std::ostream& out) const override;  // See base docs

        /** [pf optimal proposal only]  Only for PF algorithm=2 (Exact
         * "pfOptimalProposal")
         *   Select the map on which to calculate the optimal
         *    proposal distribution. Values:
         *   0: Gridmap   -> Uses Scan matching-based approximation (based on
         * Stachniss' work)
         *   1: Landmarks -> Uses matching to approximate optimal
         *   2: Beacons   -> Used for exact optimal proposal in RO-SLAM
         *   3: Pointsmap -> Uses Scan matching-based approximation with a map
         * of points (based on Stachniss' work)
         *  Default = 0
         */
        int pfOptimalProposal_mapSelection;

        /** [prediction stage][pf optimal proposal only] If
         * useICPGlobalAlign_withGrid=true, this is the minimum quality ratio
         * [0,1] of the alignment such as it will be accepted. Otherwise, raw
         * odometry is used for those bad cases (default=0.7).
          */
        float ICPGlobalAlign_MinQuality;

        /** [update stage] If the likelihood is computed through the occupancy
         * grid map, then this structure is passed to the map when updating the
         * particles weights in the update stage.
          */
        COccupancyGridMap2D::TLikelihoodOptions update_gridMapLikelihoodOptions;

        mrpt::slam::TKLDParams KLD_params;

        /** ICP parameters, used only when "PF_algorithm=2" in the particle
         * filter. */
        mrpt::slam::CICP::TConfigParams icp_params;

    } options;

    /** Constructor
      */
    CMultiMetricMapPDF(
        const bayes::CParticleFilter::TParticleFilterOptions& opts =
            bayes::CParticleFilter::TParticleFilterOptions(),
        const mrpt::maps::TSetOfMetricMapInitializers* mapsInitializers =
            nullptr,
        const TPredictionParams* predictionOptions = nullptr);

    /** Clear all elements of the maps, and restore all paths to a single
     * starting pose */
    void clear(const mrpt::poses::CPose2D& initialPose);
    /** \overload */
    void clear(const mrpt::poses::CPose3D& initialPose);
    /** Resets the map by loading an already-mapped map for past poses.
     * Current robot pose should be normally set to the last keyframe
     * in the simplemap. */
    void clear(
        const mrpt::maps::CSimpleMap& prevMap,
        const mrpt::poses::CPose3D& currentPose);

    /** Returns the estimate of the robot pose as a particles PDF for the
     * instant of time "timeStep", from 0 to N-1.
     * \sa getEstimatedPosePDF
     */
    void getEstimatedPosePDFAtTime(
        size_t timeStep,
        mrpt::poses::CPose3DPDFParticles& out_estimation) const;

    /** Returns the current estimate of the robot pose, as a particles PDF.
     * \sa getEstimatedPosePDFAtTime
     */
    void getEstimatedPosePDF(
        mrpt::poses::CPose3DPDFParticles& out_estimation) const;

    /** Returns the weighted averaged map based on the current best estimation.
     * If you need a persistent copy of this object, please use
     * "CSerializable::duplicate" and use the copy.
      * \sa Almost 100% sure you would prefer the best current map, given by
     * getCurrentMostLikelyMetricMap()
      */
    const CMultiMetricMap* getAveragedMetricMapEstimation();

    /** Returns a pointer to the current most likely map (associated to the most
     * likely particle) */
    const CMultiMetricMap* getCurrentMostLikelyMetricMap() const;

    /** Get the number of CSensoryFrame inserted into the internal member SFs */
    size_t getNumberOfObservationsInSimplemap() const { return SFs.size(); }
    /** Insert an observation to the map, at each particle's pose and to each
     * particle's metric map.
      * \param sf The SF to be inserted
      * \return true if any may was updated, false otherwise
      */
    bool insertObservation(mrpt::obs::CSensoryFrame& sf);

    /** Return the path (in absolute coordinate poses) for the i'th particle.
      * \exception On index out of bounds
      */
    void getPath(size_t i, std::deque<math::TPose3D>& out_path) const;

    /** Returns the current entropy of paths, computed as the average entropy of
     * poses along the path, where entropy of each pose estimation is computed
     * as the entropy of the gaussian approximation covariance.
      */
    double getCurrentEntropyOfPaths();

    /** Returns the joint entropy estimation over paths and maps, acording to
     * "Information Gain-based Exploration Using" by C. Stachniss, G. Grissetti
     * and W.Burgard.
      */
    double getCurrentJointEntropy();

    /** Update the poses estimation of the member "SFs" according to the current
     * path belief.
      */
    void updateSensoryFrameSequence();

    /** A logging utility: saves the current path estimation for each particle
     * in a text file (a row per particle, each 3-column-entry is a set
     * [x,y,phi], respectively).
      */
    void saveCurrentPathEstimationToTextFile(const std::string& fil);

   private:
    /** Rebuild the "expected" grid map. Used internally, do not call  */
    void rebuildAverageMap();

    /** An index [0,1] measuring how much information an observation aports to
     * the map (Typ. threshold=0.07) */
    float newInfoIndex;

   public:
    /** \name Virtual methods that the PF_implementations assume exist.
        @{ */

    // see docs in base
    mrpt::math::TPose3D getLastPose(
        const size_t i, bool& pose_is_valid) const override;

    void PF_SLAM_implementation_custom_update_particle_with_new_pose(
        CParticleDataContent* particleData,
        const mrpt::math::TPose3D& newPose) const override;

    // The base implementation is fine for this class.
    // void PF_SLAM_implementation_replaceByNewParticleSet( ...

    bool PF_SLAM_implementation_doWeHaveValidObservations(
        const CParticleList& particles,
        const mrpt::obs::CSensoryFrame* sf) const override;

    bool PF_SLAM_implementation_skipRobotMovement() const override;

    /** Evaluate the observation likelihood for one particle at a given location
     */
    double PF_SLAM_computeObservationLikelihoodForParticle(
        const mrpt::bayes::CParticleFilter::TParticleFilterOptions& PF_options,
        const size_t particleIndexForMap,
        const mrpt::obs::CSensoryFrame& observation,
        const mrpt::poses::CPose3D& x) const override;
    /** @} */

};  // End of class def.

}  // End of namespace
}  // End of namespace

#endif