00001 /* +---------------------------------------------------------------------------+ 00002 | The Mobile Robot Programming Toolkit (MRPT) C++ library | 00003 | | 00004 | http://mrpt.sourceforge.net/ | 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 CPosePDFGaussianInf_H 00029 #define CPosePDFGaussianInf_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( CPosePDFGaussianInf, CPosePDF ) 00044 00045 /** A Probability Density function (PDF) of a 2D pose \f$ p(\mathbf{x}) = [x ~ y ~ \phi ]^t \f$ as a Gaussian with a mean and the inverse of the covariance. 00046 * 00047 * This class implements a PDF as a mono-modal Gaussian distribution in its <b>information form</b>, that is, 00048 * keeping the inverse of the covariance matrix instead of the covariance matrix itself. 00049 * 00050 * This class is the dual of CPosePDFGaussian. 00051 * 00052 * \sa CPose2D, CPosePDF, CPosePDFParticles 00053 */ 00054 class BASE_IMPEXP CPosePDFGaussianInf : public CPosePDF 00055 { 00056 // This must be added to any CSerializable derived class: 00057 DEFINE_SERIALIZABLE( CPosePDFGaussianInf ) 00058 00059 protected: 00060 /** Assures the symmetry of the covariance matrix (eventually certain operations in the math-coprocessor lead to non-symmetric matrixes!) 00061 */ 00062 void assureSymmetry(); 00063 00064 public: 00065 /** @name Data fields 00066 @{ */ 00067 00068 CPose2D mean; //!< The mean value 00069 CMatrixDouble33 cov_inv; //!< The inverse of the 3x3 covariance matrix (the "information" matrix) 00070 00071 /** @} */ 00072 00073 00074 /** Default constructor (mean=all zeros, inverse covariance=all zeros -> so be careful!) */ 00075 CPosePDFGaussianInf(); 00076 00077 /** Constructor with a mean value (inverse covariance=all zeros -> so be careful!) */ 00078 explicit CPosePDFGaussianInf( const CPose2D &init_Mean ); 00079 00080 /** Constructor */ 00081 CPosePDFGaussianInf( const CPose2D &init_Mean, const CMatrixDouble33 &init_CovInv ); 00082 00083 /** Copy constructor, including transformations between other PDFs */ 00084 explicit CPosePDFGaussianInf( const CPosePDF &o ) { copyFrom( o ); } 00085 00086 /** Copy constructor, including transformations between other PDFs */ 00087 explicit CPosePDFGaussianInf( const CPose3DPDF &o ) { copyFrom( o ); } 00088 00089 00090 /** Returns an estimate of the pose, (the mean, or mathematical expectation of the PDF). 00091 * \sa getCovariance 00092 */ 00093 void getMean(CPose2D &mean_pose) const; 00094 00095 /** Returns an estimate of the pose covariance matrix (3x3 cov matrix) and the mean, both at once. 00096 * \sa getMean 00097 */ 00098 void getCovarianceAndMean(CMatrixDouble33 &cov,CPose2D &mean_point) const; 00099 00100 /** Copy operator, translating if necesary (for example, between particles and gaussian representations) 00101 */ 00102 void copyFrom(const CPosePDF &o); 00103 00104 /** Copy operator, translating if necesary (for example, between particles and gaussian representations) 00105 */ 00106 void copyFrom(const CPose3DPDF &o); 00107 00108 /** Save PDF's particles to a text file, containing the 2D pose in the first line, then the covariance matrix in next 3 lines. 00109 */ 00110 void saveToTextFile(const std::string &file) const; 00111 00112 /** This can be used to convert a PDF from local coordinates to global, providing the point (newReferenceBase) from which 00113 * "to project" the current pdf. Result PDF substituted the currently stored one in the object. 00114 */ 00115 void changeCoordinatesReference( const CPose3D &newReferenceBase ); 00116 00117 /** This can be used to convert a PDF from local coordinates to global, providing the point (newReferenceBase) from which 00118 * "to project" the current pdf. Result PDF substituted the currently stored one in the object. 00119 */ 00120 void changeCoordinatesReference( const CPose2D &newReferenceBase ); 00121 00122 /** 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$. 00123 */ 00124 void rotateCov(const double ang); 00125 00126 /** Set \f$ 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!). 00127 */ 00128 void inverseComposition( const CPosePDFGaussianInf &x, const CPosePDFGaussianInf &ref ); 00129 00130 /** 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). 00131 */ 00132 void inverseComposition( 00133 const CPosePDFGaussianInf &x1, 00134 const CPosePDFGaussianInf &x0, 00135 const CMatrixDouble33 &COV_01 00136 ); 00137 00138 /** Draws a single sample from the distribution 00139 */ 00140 void drawSingleSample( CPose2D &outPart ) const; 00141 00142 /** 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. 00143 */ 00144 void drawManySamples( size_t N, std::vector<vector_double> & outSamples ) const; 00145 00146 /** Bayesian fusion of two points gauss. distributions, then save the result in this object. 00147 * The process is as follows:<br> 00148 * - (x1,S1): Mean and variance of the p1 distribution. 00149 * - (x2,S2): Mean and variance of the p2 distribution. 00150 * - (x,S): Mean and variance of the resulting distribution. 00151 * 00152 * S = (S1<sup>-1</sup> + S2<sup>-1</sup>)<sup>-1</sup>; 00153 * x = S * ( S1<sup>-1</sup>*x1 + S2<sup>-1</sup>*x2 ); 00154 */ 00155 void bayesianFusion(const CPosePDF &p1,const CPosePDF &p2, const double &minMahalanobisDistToDrop = 0 ); 00156 00157 /** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF 00158 */ 00159 void inverse(CPosePDF &o) const; 00160 00161 /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated). */ 00162 void operator += ( const CPose2D &Ap); 00163 00164 /** Evaluates the PDF at a given point. 00165 */ 00166 double evaluatePDF( const CPose2D &x ) const; 00167 00168 /** Evaluates the ratio PDF(x) / PDF(MEAN), that is, the normalized PDF in the range [0,1]. 00169 */ 00170 double evaluateNormalizedPDF( const CPose2D &x ) const; 00171 00172 /** Computes the Mahalanobis distance between the centers of two Gaussians. 00173 */ 00174 double mahalanobisDistanceTo( const CPosePDFGaussianInf& theOther ); 00175 00176 /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated) (see formulas in jacobiansPoseComposition ). 00177 */ 00178 void operator += ( const CPosePDFGaussianInf &Ap); 00179 00180 /** Makes: thisPDF = thisPDF - Ap, where "-" is pose inverse composition (both the mean, and the covariance matrix are updated) 00181 */ 00182 inline void operator -=( const CPosePDFGaussianInf &ref ) { 00183 this->inverseComposition(*this,ref); 00184 } 00185 00186 00187 00188 /** This static method computes the pose composition Jacobians, with these formulas: 00189 \code 00190 df_dx = 00191 [ 1, 0, -sin(phi_x)*x_u-cos(phi_x)*y_u ] 00192 [ 0, 1, cos(phi_x)*x_u-sin(phi_x)*y_u ] 00193 [ 0, 0, 1 ] 00194 00195 df_du = 00196 [ cos(phi_x) , -sin(phi_x) , 0 ] 00197 [ sin(phi_x) , cos(phi_x) , 0 ] 00198 [ 0 , 0 , 1 ] 00199 \endcode 00200 */ 00201 static void jacobiansPoseComposition( 00202 const CPosePDFGaussianInf &x, 00203 const CPosePDFGaussianInf &u, 00204 CMatrixDouble33 &df_dx, 00205 CMatrixDouble33 &df_du); 00206 00207 00208 00209 }; // End of class def. 00210 00211 00212 /** Pose compose operator: RES = A (+) B , computing both the mean and the covariance */ 00213 inline CPosePDFGaussianInf operator +( const CPosePDFGaussianInf &a, const CPosePDFGaussianInf &b ) { 00214 CPosePDFGaussianInf res(a); 00215 res+=b; 00216 return res; 00217 } 00218 00219 /** Pose inverse compose operator: RES = A (-) B , computing both the mean and the covariance */ 00220 inline CPosePDFGaussianInf operator -( const CPosePDFGaussianInf &a, const CPosePDFGaussianInf &b ) { 00221 CPosePDFGaussianInf res; 00222 res.inverseComposition(a,b); 00223 return res; 00224 } 00225 00226 /** Dumps the mean and covariance matrix to a text stream. 00227 */ 00228 std::ostream BASE_IMPEXP & operator << (std::ostream & out, const CPosePDFGaussianInf& obj); 00229 00230 /** Returns the Gaussian distribution of \f$ \mathbf{C} \f$, for \f$ \mathbf{C} = \mathbf{A} \oplus \mathbf{B} \f$. 00231 */ 00232 poses::CPosePDFGaussianInf BASE_IMPEXP operator + ( const mrpt::poses::CPose2D &A, const mrpt::poses::CPosePDFGaussianInf &B ); 00233 00234 bool BASE_IMPEXP operator==(const CPosePDFGaussianInf &p1,const CPosePDFGaussianInf &p2); 00235 00236 } // End of namespace 00237 } // End of namespace 00238 00239 #endif
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