CPose3DPDFSOG.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 |
   +------------------------------------------------------------------------+ */
#pragma once
 
#include <mrpt/poses/CPose3DPDF.h>
#include <mrpt/poses/CPose3DPDFGaussian.h>
#include <mrpt/core/aligned_allocator.h>
 
namespace mrpt
{
namespace poses
{
/** Declares a class that represents a Probability Density function (PDF) of a
 * 3D(6D) pose \f$ p(\mathbf{x}) = [x ~ y ~ z ~ yaw ~ pitch ~ roll]^t \f$.
 *   This class implements that PDF as the following multi-modal Gaussian
 * distribution:
 *
 * \f$ p(\mathbf{x}) = \sum\limits_{i=1}^N \omega^i \mathcal{N}( \mathbf{x} ;
 * \bar{\mathbf{x}}^i, \mathbf{\Sigma}^i )  \f$
 *
 *  Where the number of modes N is the size of CPose3DPDFSOG::m_modes. Angles
 * are always in radians.
 *
 *  See mrpt::poses::CPose3DPDF for more details.
 * \ingroup poses_pdf_grp
 * \sa CPose3DPDF
 */
class CPose3DPDFSOG : public CPose3DPDF
{
        DEFINE_SERIALIZABLE(CPose3DPDFSOG)
 
   public:
        /** The struct for each mode:
         */
        struct TGaussianMode
        {
                TGaussianMode() : val(), log_w(0) {}
                CPose3DPDFGaussian val;
 
                /** The log-weight
                  */
                double log_w;
        };
 
        using TModesList =
                std::vector<TGaussianMode,
                                        mrpt::aligned_allocator_cpp11<TGaussianMode>>;
        using const_iterator = TModesList::const_iterator;
        using iterator = TModesList::iterator;
 
   protected:
        /** Assures the symmetry of the covariance matrix (eventually certain
         * operations in the math-coprocessor lead to non-symmetric matrixes!)
          */
        void assureSymmetry();
 
        /** Access directly to this array for modify the modes as desired.
          *  Note that no weight can be zero!!
          *  We must use pointers to satisfy the mem-alignment of the matrixes
          */
        TModesList m_modes;
 
   public:
        /** Default constructor
          * \param nModes The initial size of CPose3DPDFSOG::m_modes
          */
        CPose3DPDFSOG(size_t nModes = 1);
 
        /** Clear all the gaussian modes */
        void clear();
        /** Set the number of SOG modes */
        void resize(const size_t N);
        /** Return the number of Gaussian modes. */
        size_t size() const { return m_modes.size(); }
        /** Return whether there is any Gaussian mode. */
        bool empty() const { return m_modes.empty(); }
        iterator begin() { return m_modes.begin(); }
        iterator end() { return m_modes.end(); }
        const_iterator begin() const { return m_modes.begin(); }
        const_iterator end() const { return m_modes.end(); }
        /** Returns an estimate of the pose, (the mean, or mathematical expectation
         * of the PDF), computed as a weighted average over all m_particles. \sa
         * getCovariance */
        void getMean(CPose3D& mean_pose) const override;
        /** Returns an estimate of the pose covariance matrix (6x6 cov matrix) and
         * the mean, both at once. \sa getMean */
        void getCovarianceAndMean(
                mrpt::math::CMatrixDouble66& cov, CPose3D& mean_point) const override;
        /** Normalize the weights in m_modes such as the maximum log-weight is 0. */
        void normalizeWeights();
        /** Return the Gaussian mode with the highest likelihood (or an empty
         * Gaussian if there are no modes in this SOG) */
        void getMostLikelyMode(CPose3DPDFGaussian& outVal) const;
 
        /** Copy operator, translating if necesary (for example, between particles
         * and gaussian representations) */
        void copyFrom(const CPose3DPDF& o) override;
 
        /** Save the density to a text file, with the following format:
          *  There is one row per Gaussian "mode", and each row contains 10
         * elements:
          *   - w (The linear weight)
          *   - x_mean (gaussian mean value)
          *   - y_mean (gaussian mean value)
          *   - x_mean (gaussian mean value)
          *   - yaw_mean (gaussian mean value, in radians)
          *   - pitch_mean (gaussian mean value, in radians)
          *   - roll_mean (gaussian mean value, in radians)
          *   - C11,C22,C33,C44,C55,C66 (Covariance elements)
          *   - C12,C13,C14,C15,C16 (Covariance elements)
          *   - C23,C24,C25,C25 (Covariance elements)
          *   - C34,C35,C36 (Covariance elements)
          *   - C45,C46 (Covariance elements)
          *   - C56 (Covariance elements)
          *
         */
        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. */
        void changeCoordinatesReference(const CPose3D& newReferenceBase) override;
 
        /** Bayesian fusion of two pose distributions, then save the result in this
         * object (WARNING: Currently p1 must be a mrpt::poses::CPose3DPDFSOG object
         * and p2 a mrpt::poses::CPose3DPDFSOG object) */
        void bayesianFusion(const CPose3DPDF& p1, const CPose3DPDF& p2) override;
 
        /** Draws a single sample from the distribution */
        void drawSingleSample(CPose3D& outPart) const override;
        /** Draws a number of samples from the distribution, and saves as a list of
         * 1x6 vectors, where each row contains a (x,y,z,yaw,pitch,roll) datum */
        void drawManySamples(
                size_t N,
                std::vector<mrpt::math::CVectorDouble>& outSamples) const override;
        /** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF */
        void inverse(CPose3DPDF& o) const override;
 
        /** Append the Gaussian modes from "o" to the current set of modes of "this"
         * density */
        void appendFrom(const CPose3DPDFSOG& o);
 
};  // End of class def.
}  // End of namespace
}  // End of namespace