CPosePDFSOG.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 CPosePDFSOG_H
#define CPosePDFSOG_H

#include <mrpt/poses/CPosePDF.h>
#include <mrpt/math/CMatrixFixedNumeric.h>
#include <mrpt/math/math_frwds.h>
#include <ostream>


namespace mrpt
{
        namespace poses
        {
                // This must be added to any CSerializable derived class:
                DEFINE_SERIALIZABLE_PRE_CUSTOM_BASE( CPosePDFSOG , CPosePDF )

                /** 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 BASE_IMPEXP CPosePDFSOG : public CPosePDF
                {
                        // This must be added to any CSerializable derived class:
                        DEFINE_SERIALIZABLE( CPosePDFSOG )

                public:
                        /** The struct for each mode:
                         */
                        struct BASE_IMPEXP 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;
                          }
                        };

                        typedef mrpt::aligned_containers<TGaussianMode>::vector_t       CListGaussianModes;
                        typedef CListGaussianModes::const_iterator const_iterator;
                        typedef CListGaussianModes::iterator iterator;

                        const CListGaussianModes& getSOGModes() const { return m_modes; }

                protected:
                        void assureSymmetry(); //!< Ensures the symmetry of the covariance matrix (eventually certain operations in the math-coprocessor lead to non-symmetric matrixes!)

                        CListGaussianModes      m_modes; //!< The list of SOG modes

                 public:
                        /** Default constructor
                          * \param nModes The initial size of CPosePDFSOG::m_modes */
                        CPosePDFSOG( size_t nModes = 1 );

                        size_t size() const { return m_modes.size(); } //!< Return the number of Gaussian modes.
                        bool empty() const { return m_modes.empty(); } //!< Return whether there is any Gaussian mode.


                        void clear(); //!< Clear the list of modes

                        /** 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); }

                        void resize(const size_t N); //!< Resize the number of SOG modes

                        /** 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 );

                        void getMean(CPose2D &mean_pose) const MRPT_OVERRIDE; //!< Returns an estimate of the pose, (the mean, or mathematical expectation of the PDF) \sa getCovariance
                        void getCovarianceAndMean(mrpt::math::CMatrixDouble33 &cov,CPose2D &mean_point) const MRPT_OVERRIDE; //!< Returns an estimate of the pose covariance matrix (3x3 cov matrix) and the mean, both at once. \sa getMean
                        void getMostLikelyCovarianceAndMean(mrpt::math::CMatrixDouble33 &cov,CPose2D &mean_point) const; //!< For the most likely Gaussian mode in the SOG, returns the pose covariance matrix (3x3 cov matrix) and the mean. \sa getMean
                        void normalizeWeights(); //!< Normalize the weights in m_modes such as the maximum log-weight is 0

                        void  copyFrom(const CPosePDF &o) MRPT_OVERRIDE; //!< Copy operator, translating if necesary (for example, between particles and gaussian representations)

                        /** 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)
                          */
                        void saveToTextFile(const std::string &file) const MRPT_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 ) MRPT_OVERRIDE;

                        void rotateAllCovariances(const double &ang); //!< 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 drawSingleSample( CPose2D &outPart ) const MRPT_OVERRIDE; //!< Draws a single sample from the distribution
                        void drawManySamples( size_t N, std::vector<mrpt::math::CVectorDouble> & outSamples ) const MRPT_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 inverse(CPosePDF &o) const MRPT_OVERRIDE; //!< Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF

                        void operator += ( const mrpt::poses::CPose2D &Ap); //!< Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated).

                        double  evaluatePDF( const mrpt::poses::CPose2D &x, bool sumOverAllPhis = false ) const; //!< Evaluates the PDF at a given point.
                        double  evaluateNormalizedPDF( const mrpt::poses::CPose2D &x ) const; //!< Evaluates the ratio PDF(x) / max_PDF(x*), that is, the normalized PDF in the range [0,1].

                        /** 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 ) MRPT_OVERRIDE;

                }; // End of class def.
                DEFINE_SERIALIZABLE_POST_CUSTOM_BASE( CPosePDFSOG , CPosePDF )
        } // End of namespace
} // End of namespace
#endif