CPosePDFSOG.h

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/* +---------------------------------------------------------------------------+
   |          The Mobile Robot Programming Toolkit (MRPT) C++ library          |
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   |                       http://www.mrpt.org/                                |
   |                                                                           |
   |   Copyright (C) 2005-2012  University of Malaga                           |
   |                                                                           |
   |    This software was written by the Machine Perception and Intelligent    |
   |      Robotics Lab, University of Malaga (Spain).                          |
   |    Contact: Jose-Luis Blanco  <jlblanco@ctima.uma.es>                     |
   |                                                                           |
   |  This file is part of the MRPT project.                                   |
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   |     MRPT is free software: you can redistribute it and/or modify          |
   |     it under the terms of the GNU General Public License as published by  |
   |     the Free Software Foundation, either version 3 of the License, or     |
   |     (at your option) any later version.                                   |
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   |   MRPT is distributed in the hope that it will be useful,                 |
   |     but WITHOUT ANY WARRANTY; without even the implied warranty of        |
   |     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the         |
   |     GNU General Public License for more details.                          |
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   |     You should have received a copy of the GNU General Public License     |
   |     along with MRPT.  If not, see <http://www.gnu.org/licenses/>.         |
   |                                                                           |
   +---------------------------------------------------------------------------+ */
#ifndef CPosePDFSOG_H
#define CPosePDFSOG_H

#include <mrpt/poses/CPosePDF.h>
#include <mrpt/math/CMatrix.h>
#include <mrpt/math/CMatrixD.h>


namespace mrpt
{
        namespace poses
        {
                using namespace mrpt::math;

                // 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;
                                CMatrixDouble33 cov;

                                /** The log-weight
                                  */
                                double          log_w;

                        public:
                          EIGEN_MAKE_ALIGNED_OPERATOR_NEW
                        };

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

                protected:
                        /** Assures 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 );

                        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.

                        /** 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); }

                        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 );

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

                        /** Returns an estimate of the pose covariance matrix (3x3 cov matrix) and the mean, both at once.
                          * \sa getMean
                          */
                        void getCovarianceAndMean(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 getMostLikelyCovarianceAndMean(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);

                        /** 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;

                        /** 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 );

                        /** 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;

                        /** 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<vector_double> & outSamples ) const;

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

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

                        /** Evaluates the PDF at a given point.
                          */
                        double  evaluatePDF( const 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 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,
                                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 );


                }; // End of class def.

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

#endif