CPosePDFParticles.h

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2017, 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 CPosePDFParticles_H
#define CPosePDFParticles_H

#include <mrpt/poses/CPosePDF.h>
#include <mrpt/poses/CPoseRandomSampler.h>
#include <mrpt/bayes/CParticleFilterData.h>

namespace mrpt
{
namespace poses
{
/** Declares a class that represents a Probability Density Function (PDF) over a
 * 2D pose (x,y,phi), using a set of weighted samples.
 *
 *  This class is also the base for the implementation of Monte-Carlo
 * Localization (MCL), in mrpt::slam::CMonteCarloLocalization2D.
 *
 *  See the application "app/pf-localization" for an example of usage.
 *
 * \sa CPose2D, CPosePDF, CPoseGaussianPDF, CParticleFilterCapable
 * \ingroup poses_pdf_grp
 */
class CPosePDFParticles
        : public CPosePDF,
          public mrpt::bayes::CParticleFilterData<CPose2D>,
          public mrpt::bayes::CParticleFilterDataImpl<
                  CPosePDFParticles,
                  mrpt::bayes::CParticleFilterData<CPose2D>::CParticleList>
{
        DEFINE_SERIALIZABLE(CPosePDFParticles)

   public:
        /** Free all the memory associated to m_particles, and set the number of
         * parts = 0 */
        void clear();

        /** Constructor
          * \param M The number of m_particles.
          */
        CPosePDFParticles(size_t M = 1);

        /** Copy operator, translating if necesary (for example, between m_particles
         * and gaussian representations)
          */
        void copyFrom(const CPosePDF& o) override;

        /** Reset the PDF to a single point: All m_particles will be set exactly to
         * the supplied pose.
         * \param location The location to set all the m_particles.
         * \param particlesCount If this is set to 0 the number of m_particles
         * remains unchanged.
         *  \sa resetUniform, resetUniformFreeSpace, resetAroundSetOfPoses
         */
        void resetDeterministic(const CPose2D& location, size_t particlesCount = 0);

        /** Reset the PDF to an uniformly distributed one, inside of the defined
         * cube.
          * If particlesCount is set to -1 the number of m_particles remains
         * unchanged.
          *  \sa resetDeterministic, resetUniformFreeSpace, resetAroundSetOfPoses
          */
        void resetUniform(
                const double& x_min, const double& x_max, const double& y_min,
                const double& y_max, const double& phi_min = -M_PI,
                const double& phi_max = M_PI, const int& particlesCount = -1);

        /** Reset the PDF to a multimodal distribution over a set of "spots"
         * (x,y,phi)
          * The total number of particles will be `list_poses.size() *
         * num_particles_per_pose`.
          * \param[in] list_poses The poses (x,y,phi) around which particles will be
         * spread. Must contains at least one pose.
          * \param[in] num_particles_per_pose Number of particles to be spread
         * around each of the "spots" in list_poses. Must be >=1.
          *
          * Particles will be spread uniformly in a box of width
         * `spread_{x,y,phi_rad}` in each of
          * the three coordinates (meters, radians), so it can be understood as the
         * "initial uncertainty".
          *
          *  \sa resetDeterministic, resetUniformFreeSpace
          */
        void resetAroundSetOfPoses(
                const std::vector<mrpt::math::TPose2D>& list_poses,
                const size_t num_particles_per_pose, const double spread_x,
                const double spread_y, const double spread_phi_rad);

        /** Returns an estimate of the pose, (the mean, or mathematical expectation
         * of the PDF).
          * \sa getCovariance
          */
        void getMean(CPose2D& mean_pose) const override;

        /** Returns an estimate of the pose covariance matrix (3x3 cov matrix) and
         * the mean, both at once.
          * \sa getMean
          */
        void getCovarianceAndMean(
                mrpt::math::CMatrixDouble33& cov, CPose2D& mean_point) const override;

        /** Returns the pose of the i'th particle.
          */
        CPose2D getParticlePose(size_t i) const;

        /** Save PDF's m_particles to a text file. In each line it will go: "x y phi
         * weight"
         */
        void saveToTextFile(const std::string& file) const override;

        /** Get the m_particles count (equivalent to "particlesCount")
         */
        inline size_t size() const { return m_particles.size(); }
        /** this = p (+) this. This can be used to convert a PDF from local
         * coordinates to global, providing the point (newReferenceBase) from which
          *   "to project" the current pdf. Result PDF substituted the currently
         * stored one in the object.
          */
        void changeCoordinatesReference(const CPose3D& newReferenceBase) override;

        /** Draws a single sample from the distribution (WARNING: weights are
         * assumed to be normalized!)
          */
        void drawSingleSample(CPose2D& outPart) const override;

        /** Appends (pose-composition) a given pose "p" to each particle
          */
        void operator+=(const CPose2D& Ap);

        /** Appends (add to the list) a set of m_particles to the existing ones, and
         * then normalize weights.
          */
        void append(CPosePDFParticles& o);

        /** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF
          */
        void inverse(CPosePDF& o) const override;

        /** Returns the particle with the highest weight.
          */
        CPose2D getMostLikelyParticle() const;

        /** Bayesian fusion.
          */
        void bayesianFusion(
                const CPosePDF& p1, const CPosePDF& p2,
                const double& minMahalanobisDistToDrop = 0) override;

        /** Evaluates the PDF at a given arbitrary point as reconstructed by a
         * Parzen window.
          * \sa saveParzenPDFToTextFile
          */
        double evaluatePDF_parzen(
                const double& x, const double& y, const double& phi,
                const double& stdXY, const double& stdPhi) const;

        /** Save a text file (compatible with matlab) representing the 2D evaluation
         * of the PDF as reconstructed by a Parzen window.
          * \sa evaluatePDF_parzen
          */
        void saveParzenPDFToTextFile(
                const char* fileName, const double& x_min, const double& x_max,
                const double& y_min, const double& y_max, const double& phi,
                const double& stepSizeXY, const double& stdXY,
                const double& stdPhi) const;

};  // End of class def.

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

#endif