CPointPDFParticles.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 CPointPDFParticles_H
#define CPointPDFParticles_H
 
#include <mrpt/poses/CPointPDF.h>
#include <mrpt/math/CMatrix.h>
#include <mrpt/math/lightweight_geom_data.h>
#include <mrpt/bayes/CParticleFilterCapable.h>
#include <mrpt/bayes/CParticleFilterData.h>
 
namespace mrpt
{
namespace poses
{
/** A probability distribution of a 2D/3D point, represented as a set of random
 * samples (particles).
 * \sa CPointPDF
 * \ingroup poses_pdf_grp
 */
class CPointPDFParticles
        : public CPointPDF,
          public mrpt::bayes::CParticleFilterData<mrpt::math::TPoint3Df>,
          public mrpt::bayes::CParticleFilterDataImpl<
                  CPointPDFParticles, mrpt::bayes::CParticleFilterData<
                                                                  mrpt::math::TPoint3Df>::CParticleList>
{
        DEFINE_SERIALIZABLE(CPointPDFParticles)
 
   public:
        /** Default constructor */
        CPointPDFParticles(size_t numParticles = 1);
 
        /** Clear all the particles (free memory) */
        void clear();
 
        /** Erase all the previous particles and change the number of particles,
         * with a given initial value  */
        void setSize(
                size_t numberParticles,
                const mrpt::math::TPoint3Df& defaultValue = mrpt::math::TPoint3Df{0, 0,
                                                                                                                                                  0});
 
        /** Returns the number of particles */
        size_t size() const { return m_particles.size(); }
        /** Returns an estimate of the point, (the mean, or mathematical expectation
         * of the PDF) \sa getCovariance */
        void getMean(CPoint3D& mean_point) const override;
 
        /** Returns an estimate of the point covariance matrix (3x3 cov matrix) and
         * the mean, both at once. \sa getMean  */
        void getCovarianceAndMean(
                mrpt::math::CMatrixDouble33& cov, CPoint3D& mean_point) const override;
 
        /** Copy operator, translating if necesary (for example, between particles
         * and gaussian representations) */
        void copyFrom(const CPointPDF& o) override;
 
        /** Save PDF's particles to a text file, where each line is: X Y Z LOG_W */
        bool saveToTextFile(const std::string& file) const override;
 
        /** 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. Both the mean value and the covariance matrix
         * are updated correctly.  */
        void changeCoordinatesReference(const CPose3D& newReferenceBase) override;
 
        /** Compute the kurtosis of the distribution */
        double computeKurtosis();
 
        /** Draw a sample from the pdf */
        void drawSingleSample(CPoint3D& outSample) const override;
 
        /** Bayesian fusion of two point distributions (product of two
         * distributions->new distribution), then save the result in this object
         * (WARNING: See implementing classes to see classes that can and cannot be
         * mixtured!)
          * \param p1 The first distribution to fuse
          * \param p2 The second distribution to fuse
          * \param minMahalanobisDistToDrop If set to different of 0, the result of
         * very separate Gaussian modes (that will result in negligible components)
         * in SOGs will be dropped to reduce the number of modes in the output.
          */
        void bayesianFusion(
                const CPointPDF& p1, const CPointPDF& p2,
                const double minMahalanobisDistToDrop = 0) override;
 
};  // End of class def.
}  // End of namespace
}  // End of namespace
 
#endif