Point Cloud Library (PCL)
1.7.0
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00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Point Cloud Library (PCL) - www.pointclouds.org 00005 * Copyright (c) 2010-2012, Willow Garage, Inc. 00006 * Copyright (c) 2012-, Open Perception, Inc. 00007 * 00008 * All rights reserved. 00009 * 00010 * Redistribution and use in source and binary forms, with or without 00011 * modification, are permitted provided that the following conditions 00012 * are met: 00013 * 00014 * * Redistributions of source code must retain the above copyright 00015 * notice, this list of conditions and the following disclaimer. 00016 * * Redistributions in binary form must reproduce the above 00017 * copyright notice, this list of conditions and the following 00018 * disclaimer in the documentation and/or other materials provided 00019 * with the distribution. 00020 * * Neither the name of the copyright holder(s) nor the names of its 00021 * contributors may be used to endorse or promote products derived 00022 * from this software without specific prior written permission. 00023 * 00024 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 00025 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 00026 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 00027 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 00028 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00029 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00030 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00031 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00032 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00033 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00034 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00035 * POSSIBILITY OF SUCH DAMAGE. 00036 */ 00037 00038 #ifndef PCL_COMMON_VECTOR_AVERAGE_H 00039 #define PCL_COMMON_VECTOR_AVERAGE_H 00040 00041 #include <pcl/common/eigen.h> 00042 00043 namespace pcl 00044 { 00045 /** \brief Calculates the weighted average and the covariance matrix 00046 * 00047 * A class to calculate the weighted average and the covariance matrix of a set of vectors with given weights. 00048 * The original data is not saved. Mean and covariance are calculated iteratively. 00049 * \author Bastian Steder 00050 * \ingroup common 00051 */ 00052 template <typename real, int dimension> 00053 class VectorAverage 00054 { 00055 public: 00056 //-----CONSTRUCTOR&DESTRUCTOR----- 00057 /** Constructor - dimension gives the size of the vectors to work with. */ 00058 VectorAverage (); 00059 /** Destructor */ 00060 ~VectorAverage () {} 00061 00062 //-----METHODS----- 00063 /** Reset the object to work with a new data set */ 00064 inline void 00065 reset (); 00066 00067 /** Get the mean of the added vectors */ 00068 inline const 00069 Eigen::Matrix<real, dimension, 1>& getMean () const { return mean_;} 00070 00071 /** Get the covariance matrix of the added vectors */ 00072 inline const 00073 Eigen::Matrix<real, dimension, dimension>& getCovariance () const { return covariance_;} 00074 00075 /** Get the summed up weight of all added vectors */ 00076 inline real 00077 getAccumulatedWeight () const { return accumulatedWeight_;} 00078 00079 /** Get the number of added vectors */ 00080 inline unsigned int 00081 getNoOfSamples () { return noOfSamples_;} 00082 00083 /** Add a new sample */ 00084 inline void 00085 add (const Eigen::Matrix<real, dimension, 1>& sample, real weight=1.0); 00086 00087 /** Do Principal component analysis */ 00088 inline void 00089 doPCA (Eigen::Matrix<real, dimension, 1>& eigen_values, Eigen::Matrix<real, dimension, 1>& eigen_vector1, 00090 Eigen::Matrix<real, dimension, 1>& eigen_vector2, Eigen::Matrix<real, dimension, 1>& eigen_vector3) const; 00091 00092 /** Do Principal component analysis */ 00093 inline void 00094 doPCA (Eigen::Matrix<real, dimension, 1>& eigen_values) const; 00095 00096 /** Get the eigenvector corresponding to the smallest eigenvalue */ 00097 inline void 00098 getEigenVector1 (Eigen::Matrix<real, dimension, 1>& eigen_vector1) const; 00099 00100 //-----VARIABLES----- 00101 00102 protected: 00103 //-----METHODS----- 00104 //-----VARIABLES----- 00105 unsigned int noOfSamples_; 00106 real accumulatedWeight_; 00107 Eigen::Matrix<real, dimension, 1> mean_; 00108 Eigen::Matrix<real, dimension, dimension> covariance_; 00109 }; 00110 00111 typedef VectorAverage<float, 2> VectorAverage2f; 00112 typedef VectorAverage<float, 3> VectorAverage3f; 00113 typedef VectorAverage<float, 4> VectorAverage4f; 00114 } // END namespace 00115 00116 #include <pcl/common/impl/vector_average.hpp> 00117 00118 #endif // #ifndef PCL_VECTOR_AVERAGE_H 00119