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 CPose3DPDFGaussianInf_H 00029 #define CPose3DPDFGaussianInf_H 00030 00031 #include <mrpt/poses/CPose3DPDF.h> 00032 #include <mrpt/poses/CPosePDF.h> 00033 #include <mrpt/math/CMatrixD.h> 00034 00035 namespace mrpt 00036 { 00037 namespace poses 00038 { 00039 class CPosePDFGaussian; 00040 class CPose3DQuatPDFGaussian; 00041 00042 DEFINE_SERIALIZABLE_PRE_CUSTOM_BASE( CPose3DPDFGaussianInf , CPose3DPDF ) 00043 00044 /** Declares a class that represents a Probability Density function (PDF) of a 3D pose \f$ p(\mathbf{x}) = [x ~ y ~ z ~ yaw ~ pitch ~ roll]^t \f$ as a Gaussian described by its mean and its inverse covariance matrix. 00045 * 00046 * This class implements that PDF using a mono-modal Gaussian distribution in "information" form (inverse covariance matrix). 00047 * 00048 * Uncertainty of pose composition operations (\f$ y = x \oplus u \f$) is implemented in the method "CPose3DPDFGaussianInf::operator+=". 00049 * 00050 * For further details on implemented methods and the theory behind them, 00051 * see <a href="http://www.mrpt.org/6D_poses:equivalences_compositions_and_uncertainty" >this report</a>. 00052 * 00053 * \sa CPose3D, CPose3DPDF, CPose3DPDFParticles, CPose3DPDFGaussian 00054 * \ingroup poses_pdf_grp 00055 */ 00056 class BASE_IMPEXP CPose3DPDFGaussianInf : public CPose3DPDF 00057 { 00058 // This must be added to any CSerializable derived class: 00059 DEFINE_SERIALIZABLE( CPose3DPDFGaussianInf ) 00060 00061 protected: 00062 /** Assures the symmetry of the covariance matrix (eventually certain operations in the math-coprocessor lead to non-symmetric matrixes!) 00063 */ 00064 void assureSymmetry(); 00065 00066 public: 00067 /** @name Data fields 00068 @{ */ 00069 00070 CPose3D mean; //!< The mean value 00071 CMatrixDouble66 cov_inv; //!< The inverse of the 6x6 covariance matrix 00072 00073 /** @} */ 00074 00075 inline const CPose3D & getPoseMean() const { return mean; } 00076 inline CPose3D & getPoseMean() { return mean; } 00077 00078 /** Default constructor - mean: all zeros, inverse covariance=all zeros -> so be careful! 00079 */ 00080 CPose3DPDFGaussianInf(); 00081 00082 /** Constructor with a mean value, inverse covariance=all zeros -> so be careful! */ 00083 explicit CPose3DPDFGaussianInf( const CPose3D &init_Mean ); 00084 00085 /** Uninitialized constructor: leave all fields uninitialized - Call with UNINITIALIZED_POSE as argument 00086 */ 00087 CPose3DPDFGaussianInf(TConstructorFlags_Poses constructor_dummy_param); 00088 00089 /** Constructor with mean and inv cov. */ 00090 CPose3DPDFGaussianInf( const CPose3D &init_Mean, const CMatrixDouble66 &init_CovInv ); 00091 00092 /** Constructor from a 6D pose PDF described as a Quaternion 00093 */ 00094 explicit CPose3DPDFGaussianInf( const CPose3DQuatPDFGaussian &o); 00095 00096 /** Returns an estimate of the pose, (the mean, or mathematical expectation of the PDF). 00097 * \sa getCovariance 00098 */ 00099 void getMean(CPose3D &mean_pose) const; 00100 00101 /** Returns an estimate of the pose covariance matrix (6x6 cov matrix) and the mean, both at once. 00102 * \sa getMean 00103 */ 00104 void getCovarianceAndMean(CMatrixDouble66 &cov,CPose3D &mean_point) const; 00105 00106 /** Copy operator, translating if necesary (for example, between particles and gaussian representations) 00107 */ 00108 void copyFrom(const CPose3DPDF &o); 00109 00110 /** Copy operator, translating if necesary (for example, between particles and gaussian representations) 00111 */ 00112 void copyFrom(const CPosePDF &o); 00113 00114 /** Copy from a 6D pose PDF described as a Quaternion 00115 */ 00116 void copyFrom( const CPose3DQuatPDFGaussian &o); 00117 00118 /** Save the PDF to a text file, containing the 3D pose in the first line, then the covariance matrix in next 3 lines. 00119 */ 00120 void saveToTextFile(const std::string &file) const; 00121 00122 /** This can be used to convert a PDF from local coordinates to global, providing the point (newReferenceBase) from which 00123 * "to project" the current pdf. Result PDF substituted the currently stored one in the object. 00124 */ 00125 void changeCoordinatesReference( const CPose3D &newReferenceBase ); 00126 00127 /** Draws a single sample from the distribution 00128 */ 00129 void drawSingleSample( CPose3D &outPart ) const; 00130 00131 /** Draws a number of samples from the distribution, and saves as a list of 1x6 vectors, where each row contains a (x,y,phi) datum. 00132 */ 00133 void drawManySamples( size_t N, std::vector<vector_double> & outSamples ) const; 00134 00135 /** Bayesian fusion of two points gauss. distributions, then save the result in this object. 00136 * The process is as follows:<br> 00137 * - (x1,S1): Mean and variance of the p1 distribution. 00138 * - (x2,S2): Mean and variance of the p2 distribution. 00139 * - (x,S): Mean and variance of the resulting distribution. 00140 * 00141 * S = (S1<sup>-1</sup> + S2<sup>-1</sup>)<sup>-1</sup>; 00142 * x = S * ( S1<sup>-1</sup>*x1 + S2<sup>-1</sup>*x2 ); 00143 */ 00144 void bayesianFusion( const CPose3DPDF &p1, const CPose3DPDF &p2 ); 00145 00146 /** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF 00147 */ 00148 void inverse(CPose3DPDF &o) const; 00149 00150 /** Unary - operator, returns the PDF of the inverse pose. */ 00151 inline CPose3DPDFGaussianInf operator -() const 00152 { 00153 CPose3DPDFGaussianInf p(UNINITIALIZED_POSE); 00154 this->inverse(p); 00155 return p; 00156 } 00157 00158 /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated). 00159 */ 00160 void operator += ( const CPose3D &Ap); 00161 00162 /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated). 00163 */ 00164 void operator += ( const CPose3DPDFGaussianInf &Ap); 00165 00166 /** Makes: thisPDF = thisPDF - Ap, where "-" is pose inverse composition (both the mean, and the covariance matrix are updated). 00167 */ 00168 void operator -= ( const CPose3DPDFGaussianInf &Ap); 00169 00170 /** Evaluates the PDF at a given point. 00171 */ 00172 double evaluatePDF( const CPose3D &x ) const; 00173 00174 /** Evaluates the ratio PDF(x) / PDF(MEAN), that is, the normalized PDF in the range [0,1]. 00175 */ 00176 double evaluateNormalizedPDF( const CPose3D &x ) const; 00177 00178 /** Computes the Mahalanobis distance between the centers of two Gaussians. 00179 * The variables with a variance exactly equal to 0 are not taken into account in the process, but 00180 * "infinity" is returned if the corresponding elements are not exactly equal. 00181 */ 00182 double mahalanobisDistanceTo( const CPose3DPDFGaussianInf& theOther); 00183 00184 /** This static method computes the pose composition Jacobians, with these formulas: 00185 \code 00186 df_dx = 00187 [ 1, 0, 0, -sin(yaw)*cos(p)*xu+(-sin(yaw)*sin(p)*sin(r)-cos(yaw)*cos(r))*yu+(-sin(yaw)*sin(p)*cos(r)+cos(yaw)*sin(r))*zu, -cos(yaw)*sin(p)*xu+cos(yaw)*cos(p)*sin(r)*yu+cos(yaw)*cos(p)*cos(r)*zu, (cos(yaw)*sin(p)*cos(r)+sin(yaw)*sin(r))*yu+(-cos(yaw)*sin(p)*sin(r)+sin(yaw)*cos(r))*zu] 00188 [ 0, 1, 0, cos(yaw)*cos(p)*xu+(cos(yaw)*sin(p)*sin(r)-sin(yaw)*cos(r))*yu+(cos(yaw)*sin(p)*cos(r)+sin(yaw)*sin(r))*zu, -sin(yaw)*sin(p)*xu+sin(yaw)*cos(p)*sin(r)*yu+sin(yaw)*cos(p)*cos(r)*zu, (sin(yaw)*sin(p)*cos(r)-cos(yaw)*sin(r))*yu+(-sin(yaw)*sin(p)*sin(r)-cos(yaw)*cos(r))*zu] 00189 [ 0, 0, 1, 0, -cos(p)*xu-sin(p)*sin(r)*yu-sin(p)*cos(r)*zu, cos(p)*cos(r)*yu-cos(p)*sin(r)*zu] 00190 [ 0, 0, 0, 1, 0, 0] 00191 [ 0, 0, 0, 0, 1, 0] 00192 [ 0, 0, 0, 0, 0, 1] 00193 00194 df_du = 00195 [ cos(yaw)*cos(p), cos(yaw)*sin(p)*sin(r)-sin(yaw)*cos(r), cos(yaw)*sin(p)*cos(r)+sin(yaw)*sin(r), 0, 0, 0] 00196 [ sin(yaw)*cos(p), sin(yaw)*sin(p)*sin(r)+cos(yaw)*cos(r), sin(yaw)*sin(p)*cos(r)-cos(yaw)*sin(r), 0, 0, 0] 00197 [ -sin(p), cos(p)*sin(r), cos(p)*cos(r), 0, 0, 0] 00198 [ 0, 0, 0, 1, 0, 0] 00199 [ 0, 0, 0, 0, 1, 0] 00200 [ 0, 0, 0, 0, 0, 1] 00201 \endcode 00202 */ 00203 static void jacobiansPoseComposition( 00204 const CPose3D &x, 00205 const CPose3D &u, 00206 CMatrixDouble66 &df_dx, 00207 CMatrixDouble66 &df_du); 00208 00209 /** Returns a 3x3 matrix with submatrix of the inverse covariance for the variables (x,y,yaw) only. 00210 */ 00211 void getInvCovSubmatrix2D( CMatrixDouble &out_cov ) const; 00212 00213 }; // End of class def. 00214 00215 00216 /** Pose composition for two 3D pose Gaussians \sa CPose3DPDFGaussian::operator += */ 00217 inline CPose3DPDFGaussianInf operator +( const CPose3DPDFGaussianInf &x, const CPose3DPDFGaussianInf &u ) 00218 { 00219 CPose3DPDFGaussianInf res(x); 00220 res+=u; 00221 return res; 00222 } 00223 00224 /** Pose composition for two 3D pose Gaussians \sa CPose3DPDFGaussianInf::operator -= */ 00225 inline CPose3DPDFGaussianInf operator -( const CPose3DPDFGaussianInf &x, const CPose3DPDFGaussianInf &u ) 00226 { 00227 CPose3DPDFGaussianInf res(x); 00228 res-=u; 00229 return res; 00230 } 00231 00232 /** Dumps the mean and covariance matrix to a text stream. 00233 */ 00234 std::ostream BASE_IMPEXP & operator << (std::ostream & out, const CPose3DPDFGaussianInf& obj); 00235 00236 bool BASE_IMPEXP operator==(const CPose3DPDFGaussianInf &p1,const CPose3DPDFGaussianInf &p2); 00237 00238 } // End of namespace 00239 } // End of namespace 00240 00241 #endif
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