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 CPosePDFGaussian_H 00029 #define CPosePDFGaussian_H 00030 00031 #include <mrpt/poses/CPosePDF.h> 00032 #include <mrpt/math/CMatrixFixedNumeric.h> 00033 00034 namespace mrpt 00035 { 00036 namespace poses 00037 { 00038 using namespace mrpt::math; 00039 00040 class CPose3DPDF; 00041 00042 // This must be added to any CSerializable derived class: 00043 DEFINE_SERIALIZABLE_PRE_CUSTOM_BASE( CPosePDFGaussian, 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 * 00047 * This class implements that PDF using a mono-modal Gaussian distribution. See mrpt::poses::CPosePDF for more details. 00048 * 00049 * \sa CPose2D, CPosePDF, CPosePDFParticles 00050 * \ingroup poses_pdf_grp 00051 */ 00052 class BASE_IMPEXP CPosePDFGaussian : public CPosePDF 00053 { 00054 // This must be added to any CSerializable derived class: 00055 DEFINE_SERIALIZABLE( CPosePDFGaussian ) 00056 00057 protected: 00058 /** Assures the symmetry of the covariance matrix (eventually certain operations in the math-coprocessor lead to non-symmetric matrixes!) 00059 */ 00060 void assureSymmetry(); 00061 00062 public: 00063 /** @name Data fields 00064 @{ */ 00065 00066 CPose2D mean; //!< The mean value 00067 CMatrixDouble33 cov; //!< The 3x3 covariance matrix 00068 00069 /** @} */ 00070 00071 inline const CPose2D & getPoseMean() const { return mean; } 00072 inline CPose2D & getPoseMean() { return mean; } 00073 00074 /** Default constructor 00075 */ 00076 CPosePDFGaussian(); 00077 00078 /** Constructor 00079 */ 00080 explicit CPosePDFGaussian( const CPose2D &init_Mean ); 00081 00082 /** Constructor 00083 */ 00084 CPosePDFGaussian( const CPose2D &init_Mean, const CMatrixDouble33 &init_Cov ); 00085 00086 /** Copy constructor, including transformations between other PDFs */ 00087 explicit CPosePDFGaussian( const CPosePDF &o ) { copyFrom( o ); } 00088 00089 /** Copy constructor, including transformations between other PDFs */ 00090 explicit CPosePDFGaussian( const CPose3DPDF &o ) { copyFrom( o ); } 00091 00092 /** Returns an estimate of the pose, (the mean, or mathematical expectation of the PDF). 00093 * \sa getCovariance 00094 */ 00095 void getMean(CPose2D &mean_pose) const; 00096 00097 /** Returns an estimate of the pose covariance matrix (3x3 cov matrix) and the mean, both at once. 00098 * \sa getMean 00099 */ 00100 void getCovarianceAndMean(CMatrixDouble33 &cov,CPose2D &mean_point) const; 00101 00102 /** Copy operator, translating if necesary (for example, between particles and gaussian representations) 00103 */ 00104 void copyFrom(const CPosePDF &o); 00105 00106 /** Copy operator, translating if necesary (for example, between particles and gaussian representations) 00107 */ 00108 void copyFrom(const CPose3DPDF &o); 00109 00110 /** Save PDF's particles to a text file, containing the 2D pose in the first line, then the covariance matrix in next 3 lines. 00111 */ 00112 void saveToTextFile(const std::string &file) const; 00113 00114 /** This can be used to convert a PDF from local coordinates to global, providing the point (newReferenceBase) from which 00115 * "to project" the current pdf. Result PDF substituted the currently stored one in the object. 00116 */ 00117 void changeCoordinatesReference( const CPose3D &newReferenceBase ); 00118 00119 /** This can be used to convert a PDF from local coordinates to global, providing the point (newReferenceBase) from which 00120 * "to project" the current pdf. Result PDF substituted the currently stored one in the object. 00121 */ 00122 void changeCoordinatesReference( const CPose2D &newReferenceBase ); 00123 00124 /** Rotate the covariance matrix by replacing it 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$. 00125 */ 00126 void rotateCov(const double ang); 00127 00128 /** Set \f$ this = x1 \ominus x0 \f$ , computing the mean using the "-" operator and the covariances through the corresponding Jacobians (For 'x0' and 'x1' being independent variables!). 00129 */ 00130 void inverseComposition( const CPosePDFGaussian &x, const CPosePDFGaussian &ref ); 00131 00132 /** Set \f$ this = x1 \ominus x0 \f$ , computing the mean using the "-" operator and the covariances through the corresponding Jacobians (Given the 3x3 cross-covariance matrix of variables x0 and x1). 00133 */ 00134 void inverseComposition( 00135 const CPosePDFGaussian &x1, 00136 const CPosePDFGaussian &x0, 00137 const CMatrixDouble33 &COV_01 00138 ); 00139 00140 /** Draws a single sample from the distribution 00141 */ 00142 void drawSingleSample( CPose2D &outPart ) const; 00143 00144 /** 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. 00145 */ 00146 void drawManySamples( size_t N, std::vector<vector_double> & outSamples ) const; 00147 00148 /** Bayesian fusion of two points gauss. distributions, then save the result in this object. 00149 * The process is as follows:<br> 00150 * - (x1,S1): Mean and variance of the p1 distribution. 00151 * - (x2,S2): Mean and variance of the p2 distribution. 00152 * - (x,S): Mean and variance of the resulting distribution. 00153 * 00154 * S = (S1<sup>-1</sup> + S2<sup>-1</sup>)<sup>-1</sup>; 00155 * x = S * ( S1<sup>-1</sup>*x1 + S2<sup>-1</sup>*x2 ); 00156 */ 00157 void bayesianFusion(const CPosePDF &p1,const CPosePDF &p2, const double &minMahalanobisDistToDrop = 0 ); 00158 00159 /** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF 00160 */ 00161 void inverse(CPosePDF &o) const; 00162 00163 /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated). 00164 */ 00165 void operator += ( const CPose2D &Ap); 00166 00167 /** Evaluates the PDF at a given point. 00168 */ 00169 double evaluatePDF( const CPose2D &x ) const; 00170 00171 /** Evaluates the ratio PDF(x) / PDF(MEAN), that is, the normalized PDF in the range [0,1]. 00172 */ 00173 double evaluateNormalizedPDF( const CPose2D &x ) const; 00174 00175 /** Computes the Mahalanobis distance between the centers of two Gaussians. 00176 */ 00177 double mahalanobisDistanceTo( const CPosePDFGaussian& theOther ); 00178 00179 /** Substitutes the diagonal elements if (square) they are below some given minimum values (Use this before bayesianFusion, for example, to avoid inversion of singular matrixes, etc...) 00180 */ 00181 void assureMinCovariance( const double & minStdXY, const double &minStdPhi ); 00182 00183 /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated) (see formulas in jacobiansPoseComposition ). 00184 */ 00185 void operator += ( const CPosePDFGaussian &Ap); 00186 00187 /** Makes: thisPDF = thisPDF - Ap, where "-" is pose inverse composition (both the mean, and the covariance matrix are updated) 00188 */ 00189 inline void operator -=( const CPosePDFGaussian &ref ) { 00190 this->inverseComposition(*this,ref); 00191 } 00192 00193 00194 00195 /** This static method computes the pose composition Jacobians, with these formulas: 00196 \code 00197 df_dx = 00198 [ 1, 0, -sin(phi_x)*x_u-cos(phi_x)*y_u ] 00199 [ 0, 1, cos(phi_x)*x_u-sin(phi_x)*y_u ] 00200 [ 0, 0, 1 ] 00201 00202 df_du = 00203 [ cos(phi_x) , -sin(phi_x) , 0 ] 00204 [ sin(phi_x) , cos(phi_x) , 0 ] 00205 [ 0 , 0 , 1 ] 00206 \endcode 00207 */ 00208 static void jacobiansPoseComposition( 00209 const CPosePDFGaussian &x, 00210 const CPosePDFGaussian &u, 00211 CMatrixDouble33 &df_dx, 00212 CMatrixDouble33 &df_du); 00213 00214 00215 00216 }; // End of class def. 00217 00218 00219 /** Pose compose operator: RES = A (+) B , computing both the mean and the covariance */ 00220 inline CPosePDFGaussian operator +( const CPosePDFGaussian &a, const CPosePDFGaussian &b ) { 00221 CPosePDFGaussian res(a); 00222 res+=b; 00223 return res; 00224 } 00225 00226 /** Pose inverse compose operator: RES = A (-) B , computing both the mean and the covariance */ 00227 inline CPosePDFGaussian operator -( const CPosePDFGaussian &a, const CPosePDFGaussian &b ) { 00228 CPosePDFGaussian res; 00229 res.inverseComposition(a,b); 00230 return res; 00231 } 00232 00233 /** Dumps the mean and covariance matrix to a text stream. 00234 */ 00235 std::ostream BASE_IMPEXP & operator << (std::ostream & out, const CPosePDFGaussian& obj); 00236 00237 /** Returns the Gaussian distribution of \f$ \mathbf{C} \f$, for \f$ \mathbf{C} = \mathbf{A} \oplus \mathbf{B} \f$. 00238 */ 00239 poses::CPosePDFGaussian BASE_IMPEXP operator + ( const mrpt::poses::CPose2D &A, const mrpt::poses::CPosePDFGaussian &B ); 00240 00241 bool BASE_IMPEXP operator==(const CPosePDFGaussian &p1,const CPosePDFGaussian &p2); 00242 00243 } // End of namespace 00244 } // End of namespace 00245 00246 #endif
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