00001 /* +---------------------------------------------------------------------------+ 00002 | The Mobile Robot Programming Toolkit (MRPT) C++ library | 00003 | | 00004 | http://www.mrpt.org/ | 00005 | | 00006 | Copyright (C) 2005-2011 University of Malaga | 00007 | | 00008 | This software was written by the Machine Perception and Intelligent | 00009 | Robotics Lab, University of Malaga (Spain). | 00010 | Contact: Jose-Luis Blanco <jlblanco@ctima.uma.es> | 00011 | | 00012 | This file is part of the MRPT project. | 00013 | | 00014 | MRPT is free software: you can redistribute it and/or modify | 00015 | it under the terms of the GNU General Public License as published by | 00016 | the Free Software Foundation, either version 3 of the License, or | 00017 | (at your option) any later version. | 00018 | | 00019 | MRPT is distributed in the hope that it will be useful, | 00020 | but WITHOUT ANY WARRANTY; without even the implied warranty of | 00021 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | 00022 | GNU General Public License for more details. | 00023 | | 00024 | You should have received a copy of the GNU General Public License | 00025 | along with MRPT. If not, see <http://www.gnu.org/licenses/>. | 00026 | | 00027 +---------------------------------------------------------------------------+ */ 00028 #ifndef CPosePDFSOG_H 00029 #define CPosePDFSOG_H 00030 00031 #include <mrpt/poses/CPosePDF.h> 00032 #include <mrpt/math/CMatrix.h> 00033 #include <mrpt/math/CMatrixD.h> 00034 00035 00036 namespace mrpt 00037 { 00038 namespace poses 00039 { 00040 using namespace mrpt::math; 00041 00042 // This must be added to any CSerializable derived class: 00043 DEFINE_SERIALIZABLE_PRE_CUSTOM_BASE( CPosePDFSOG , CPosePDF ) 00044 00045 /** Declares a class that represents a Probability Density function (PDF) of a 2D pose \f$ p(\mathbf{x}) = [x ~ y ~ \phi ]^t \f$. 00046 * This class implements that PDF as the following multi-modal Gaussian distribution: 00047 * 00048 * \f$ p(\mathbf{x}) = \sum\limits_{i=1}^N \omega^i \mathcal{N}( \mathbf{x} ; \bar{\mathbf{x}}^i, \mathbf{\Sigma}^i ) \f$ 00049 * 00050 * Where the number of modes N is the size of CPosePDFSOG::m_modes 00051 * 00052 * See mrpt::poses::CPosePDF for more details. 00053 * 00054 * \sa CPose2D, CPosePDF, CPosePDFParticles 00055 * \ingroup poses_pdf_grp 00056 */ 00057 class BASE_IMPEXP CPosePDFSOG : public CPosePDF 00058 { 00059 // This must be added to any CSerializable derived class: 00060 DEFINE_SERIALIZABLE( CPosePDFSOG ) 00061 00062 public: 00063 /** The struct for each mode: 00064 */ 00065 struct BASE_IMPEXP TGaussianMode 00066 { 00067 TGaussianMode() : 00068 mean(), 00069 cov(), 00070 log_w(0) 00071 { } 00072 00073 CPose2D mean; 00074 CMatrixDouble33 cov; 00075 00076 /** The log-weight 00077 */ 00078 double log_w; 00079 00080 public: 00081 EIGEN_MAKE_ALIGNED_OPERATOR_NEW 00082 }; 00083 00084 typedef mrpt::aligned_containers<TGaussianMode>::vector_t CListGaussianModes; 00085 typedef CListGaussianModes::const_iterator const_iterator; 00086 typedef CListGaussianModes::iterator iterator; 00087 00088 protected: 00089 /** Assures the symmetry of the covariance matrix (eventually certain operations in the math-coprocessor lead to non-symmetric matrixes!) 00090 */ 00091 void assureSymmetry(); 00092 00093 /** The list of SOG modes */ 00094 CListGaussianModes m_modes; 00095 00096 public: 00097 /** Default constructor 00098 * \param nModes The initial size of CPosePDFSOG::m_modes 00099 */ 00100 CPosePDFSOG( size_t nModes = 1 ); 00101 00102 size_t size() const { return m_modes.size(); } //!< Return the number of Gaussian modes. 00103 bool empty() const { return m_modes.empty(); } //!< Return whether there is any Gaussian mode. 00104 00105 /** Clear the list of modes */ 00106 void clear(); 00107 00108 /** Access to individual beacons */ 00109 const TGaussianMode& operator [](size_t i) const { 00110 ASSERT_(i<m_modes.size()) 00111 return m_modes[i]; 00112 } 00113 /** Access to individual beacons */ 00114 TGaussianMode& operator [](size_t i) { 00115 ASSERT_(i<m_modes.size()) 00116 return m_modes[i]; 00117 } 00118 00119 /** Access to individual beacons */ 00120 const TGaussianMode& get(size_t i) const { 00121 ASSERT_(i<m_modes.size()) 00122 return m_modes[i]; 00123 } 00124 /** Access to individual beacons */ 00125 TGaussianMode& get(size_t i) { 00126 ASSERT_(i<m_modes.size()) 00127 return m_modes[i]; 00128 } 00129 00130 /** Inserts a copy of the given mode into the SOG */ 00131 void push_back(const TGaussianMode& m) { 00132 m_modes.push_back(m); 00133 } 00134 00135 iterator begin() { return m_modes.begin(); } 00136 iterator end() { return m_modes.end(); } 00137 const_iterator begin() const { return m_modes.begin(); } 00138 const_iterator end()const { return m_modes.end(); } 00139 00140 iterator erase(iterator i) { return m_modes.erase(i); } 00141 00142 void resize(const size_t N); //!< Resize the number of SOG modes 00143 00144 /** Merge very close modes so the overall number of modes is reduced while preserving the total distribution. 00145 * This method uses the approach described in the paper: 00146 * - "Kullback-Leibler Approach to Gaussian Mixture Reduction" AR Runnalls. IEEE Transactions on Aerospace and Electronic Systems, 2007. 00147 * 00148 * \param max_KLd The maximum KL-divergence to consider the merge of two nodes (and then stops the process). 00149 */ 00150 void mergeModes( double max_KLd = 0.5, bool verbose = false ); 00151 00152 /** Returns an estimate of the pose, (the mean, or mathematical expectation of the PDF). 00153 * \sa getCovariance 00154 */ 00155 void getMean(CPose2D &mean_pose) const; 00156 00157 /** Returns an estimate of the pose covariance matrix (3x3 cov matrix) and the mean, both at once. 00158 * \sa getMean 00159 */ 00160 void getCovarianceAndMean(CMatrixDouble33 &cov,CPose2D &mean_point) const; 00161 00162 /** For the most likely Gaussian mode in the SOG, returns the pose covariance matrix (3x3 cov matrix) and the mean. 00163 * \sa getMean 00164 */ 00165 void getMostLikelyCovarianceAndMean(CMatrixDouble33 &cov,CPose2D &mean_point) const; 00166 00167 /** Normalize the weights in m_modes such as the maximum log-weight is 0. 00168 */ 00169 void normalizeWeights(); 00170 00171 /** Copy operator, translating if necesary (for example, between particles and gaussian representations) 00172 */ 00173 void copyFrom(const CPosePDF &o); 00174 00175 /** Save the density to a text file, with the following format: 00176 * There is one row per Gaussian "mode", and each row contains 10 elements: 00177 * - w (The weight) 00178 * - x_mean (gaussian mean value) 00179 * - y_mean (gaussian mean value) 00180 * - phi_mean (gaussian mean value) 00181 * - C11 (Covariance elements) 00182 * - C22 (Covariance elements) 00183 * - C33 (Covariance elements) 00184 * - C12 (Covariance elements) 00185 * - C13 (Covariance elements) 00186 * - C23 (Covariance elements) 00187 * 00188 */ 00189 void saveToTextFile(const std::string &file) const; 00190 00191 /** This can be used to convert a PDF from local coordinates to global, providing the point (newReferenceBase) from which 00192 * "to project" the current pdf. Result PDF substituted the currently stored one in the object. 00193 */ 00194 void changeCoordinatesReference(const CPose3D &newReferenceBase ); 00195 00196 /** 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$. 00197 */ 00198 void rotateAllCovariances(const double &ang); 00199 00200 /** Draws a single sample from the distribution 00201 */ 00202 void drawSingleSample( CPose2D &outPart ) const; 00203 00204 /** 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. 00205 */ 00206 void drawManySamples( size_t N, std::vector<vector_double> & outSamples ) const; 00207 00208 /** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF 00209 */ 00210 void inverse(CPosePDF &o) const; 00211 00212 /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated). 00213 */ 00214 void operator += ( const CPose2D &Ap); 00215 00216 /** Evaluates the PDF at a given point. 00217 */ 00218 double evaluatePDF( const CPose2D &x, bool sumOverAllPhis = false ) const; 00219 00220 /** Evaluates the ratio PDF(x) / max_PDF(x*), that is, the normalized PDF in the range [0,1]. 00221 */ 00222 double evaluateNormalizedPDF( const CPose2D &x ) const; 00223 00224 /** 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). 00225 */ 00226 void evaluatePDFInArea( 00227 const double & x_min, 00228 const double & x_max, 00229 const double & y_min, 00230 const double & y_max, 00231 const double & resolutionXY, 00232 const double & phi, 00233 CMatrixD &outMatrix, 00234 bool sumOverAllPhis = false ); 00235 00236 /** 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) 00237 */ 00238 void bayesianFusion(const CPosePDF &p1,const CPosePDF &p2, const double &minMahalanobisDistToDrop=0 ); 00239 00240 00241 }; // End of class def. 00242 00243 } // End of namespace 00244 } // End of namespace 00245 00246 #endif
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