CPosePDFSOG.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 CPosePDFSOG_H
#define CPosePDFSOG_H
 
#include <mrpt/poses/CPosePDF.h>
#include <mrpt/math/CMatrixFixedNumeric.h>
#include <mrpt/math/math_frwds.h>
#include <mrpt/core/aligned_std_vector.h>
#include <ostream>
 
namespace mrpt
{
namespace poses
{
/** Declares a class that represents a Probability Density  function (PDF) of a
 * 2D pose \f$ p(\mathbf{x}) = [x ~ y ~ \phi ]^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 CPosePDFSOG::m_modes
 *
 *  See mrpt::poses::CPosePDF for more details.
 *
 * \sa CPose2D, CPosePDF, CPosePDFParticles
 * \ingroup poses_pdf_grp
 */
class CPosePDFSOG : public CPosePDF
{
        DEFINE_SERIALIZABLE(CPosePDFSOG)
 
   public:
        /** The struct for each mode:
         */
        struct TGaussianMode
        {
                TGaussianMode() : mean(), cov(), log_w(0) {}
                CPose2D mean;
                mrpt::math::CMatrixDouble33 cov;
 
                /** The log-weight
                  */
                double log_w;
 
           public:
                MRPT_MAKE_ALIGNED_OPERATOR_NEW;
 
                friend std::ostream& operator<<(
                        std::ostream& o, const TGaussianMode& mode)
                {
                        o << "Mean: " << mode.mean << std::endl
                          << "Covariance: " << std::endl
                          << mode.cov << std::endl
                          << "Log-weight: " << mode.log_w << std::endl;
                        return o;
                }
        };
 
        using CListGaussianModes = mrpt::aligned_std_vector<TGaussianMode>;
        using const_iterator = CListGaussianModes::const_iterator;
        using iterator = CListGaussianModes::iterator;
 
        const CListGaussianModes& getSOGModes() const { return m_modes; }
   protected:
        /** Ensures the symmetry of the covariance matrix (eventually certain
         * operations in the math-coprocessor lead to non-symmetric matrixes!) */
        void assureSymmetry();
 
        /** The list of SOG modes */
        CListGaussianModes m_modes;
 
   public:
        /** Default constructor
          * \param nModes The initial size of CPosePDFSOG::m_modes */
        CPosePDFSOG(size_t nModes = 1);
 
        /** 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(); }
        /** Clear the list of modes */
        void clear();
 
        /** Access to individual beacons */
        const TGaussianMode& operator[](size_t i) const
        {
                ASSERT_(i < m_modes.size());
                return m_modes[i];
        }
        /** Access to individual beacons */
        TGaussianMode& operator[](size_t i)
        {
                ASSERT_(i < m_modes.size());
                return m_modes[i];
        }
 
        /** Access to individual beacons */
        const TGaussianMode& get(size_t i) const
        {
                ASSERT_(i < m_modes.size());
                return m_modes[i];
        }
        /** Access to individual beacons */
        TGaussianMode& get(size_t i)
        {
                ASSERT_(i < m_modes.size());
                return m_modes[i];
        }
 
        /** Inserts a copy of the given mode into the SOG */
        void push_back(const TGaussianMode& m) { m_modes.push_back(m); }
        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(); }
        iterator erase(iterator i) { return m_modes.erase(i); }
        /** Resize the number of SOG modes */
        void resize(const size_t N);
 
        /** Merge very close modes so the overall number of modes is reduced while
         * preserving the total distribution.
          *  This method uses the approach described in the paper:
          *  - "Kullback-Leibler Approach to Gaussian Mixture Reduction" AR
         * Runnalls. IEEE Transactions on Aerospace and Electronic Systems, 2007.
          *
          *  \param max_KLd The maximum KL-divergence to consider the merge of two
         * nodes (and then stops the process).
          */
        void mergeModes(double max_KLd = 0.5, bool verbose = false);
 
        /** 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;
        /** For the most likely Gaussian mode in the SOG, returns the pose
         * covariance matrix (3x3 cov matrix) and the mean. \sa getMean */
        void getMostLikelyCovarianceAndMean(
                mrpt::math::CMatrixDouble33& cov, CPose2D& mean_point) const;
        /** Normalize the weights in m_modes such as the maximum log-weight is 0 */
        void normalizeWeights();
 
        /** Copy operator, translating if necesary (for example, between particles
         * and gaussian representations) */
        void copyFrom(const CPosePDF& 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 weight)
          *   - x_mean (gaussian mean value)
          *   - y_mean (gaussian mean value)
          *   - phi_mean (gaussian mean value)
          *   - C11 (Covariance elements)
          *   - C22 (Covariance elements)
          *   - C33 (Covariance elements)
          *   - C12 (Covariance elements)
          *   - C13 (Covariance elements)
          *   - C23 (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;
 
        /** Rotate all the covariance matrixes by replacing them by \f$
         * \mathbf{R}~\mathbf{COV}~\mathbf{R}^t \f$, where \f$ \mathbf{R} = \left[
         * \begin{array}{ccc} \cos\alpha & -\sin\alpha & 0 \\ \sin\alpha &
         * \cos\alpha & 0 \\ 0 & 0 & 1 \end{array}\right] \f$ */
        void rotateAllCovariances(const double& ang);
        /** Draws a single sample from the distribution */
        void drawSingleSample(CPose2D& outPart) const override;
        /** Draws a number of samples from the distribution, and saves as a list of
         * 1x3 vectors, where each row contains a (x,y,phi) 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(CPosePDF& o) const override;
 
        /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the
         * mean, and the covariance matrix are updated). */
        void operator+=(const mrpt::poses::CPose2D& Ap);
 
        /** Evaluates the PDF at a given point. */
        double evaluatePDF(
                const mrpt::poses::CPose2D& x, bool sumOverAllPhis = false) const;
        /** Evaluates the ratio PDF(x) / max_PDF(x*), that is, the normalized PDF in
         * the range [0,1]. */
        double evaluateNormalizedPDF(const mrpt::poses::CPose2D& x) const;
 
        /** Evaluates the PDF within a rectangular grid (and a fixed orientation)
         * and saves the result in a matrix (each row contains values for a fixed
         * y-coordinate value). */
        void evaluatePDFInArea(
                const double& x_min, const double& x_max, const double& y_min,
                const double& y_max, const double& resolutionXY, const double& phi,
                mrpt::math::CMatrixD& outMatrix, bool sumOverAllPhis = false);
 
        /** Bayesian fusion of two pose distributions, then save the result in this
         * object (WARNING: Currently p1 must be a mrpt::poses::CPosePDFSOG object
         * and p2 a mrpt::poses::CPosePDFGaussian object) */
        void bayesianFusion(
                const CPosePDF& p1, const CPosePDF& p2,
                const double minMahalanobisDistToDrop = 0) override;
 
};  // End of class def.
}  // End of namespace
}  // End of namespace
#endif