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Point Cloud Library (PCL)
1.5.1
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00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Point Cloud Library (PCL) - www.pointclouds.org 00005 * Copyright (c) 2010-2011, Willow Garage, Inc. 00006 * 00007 * All rights reserved. 00008 * 00009 * Redistribution and use in source and binary forms, with or without 00010 * modification, are permitted provided that the following conditions 00011 * are met: 00012 * 00013 * * Redistributions of source code must retain the above copyright 00014 * notice, this list of conditions and the following disclaimer. 00015 * * Redistributions in binary form must reproduce the above 00016 * copyright notice, this list of conditions and the following 00017 * disclaimer in the documentation and/or other materials provided 00018 * with the distribution. 00019 * * Neither the name of Willow Garage, Inc. nor the names of its 00020 * contributors may be used to endorse or promote products derived 00021 * from this software without specific prior written permission. 00022 * 00023 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 00024 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 00025 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 00026 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 00027 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00028 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00029 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00030 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00031 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00032 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00033 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00034 * POSSIBILITY OF SUCH DAMAGE. 00035 * 00036 * 00037 */ 00038 #ifndef PCL_REGISTRATION_CORRESPONDENCE_REJECTION_DISTANCE_H_ 00039 #define PCL_REGISTRATION_CORRESPONDENCE_REJECTION_DISTANCE_H_ 00040 00041 #include <pcl/registration/correspondence_rejection.h> 00042 #include <pcl/point_cloud.h> 00043 #include <pcl/kdtree/kdtree_flann.h> 00044 00045 namespace pcl 00046 { 00047 namespace registration 00048 { 00061 class CorrespondenceRejectorDistance: public CorrespondenceRejector 00062 { 00063 using CorrespondenceRejector::input_correspondences_; 00064 using CorrespondenceRejector::rejection_name_; 00065 using CorrespondenceRejector::getClassName; 00066 00067 public: 00068 00070 CorrespondenceRejectorDistance () : max_distance_(std::numeric_limits<float>::max ()), 00071 data_container_ () 00072 { 00073 rejection_name_ = "CorrespondenceRejectorDistance"; 00074 } 00075 00080 inline void 00081 getRemainingCorrespondences (const pcl::Correspondences& original_correspondences, 00082 pcl::Correspondences& remaining_correspondences); 00083 00089 virtual inline void 00090 setMaximumDistance (float distance) { max_distance_ = distance * distance; }; 00091 00093 inline float 00094 getMaximumDistance () { return std::sqrt (max_distance_); }; 00095 00100 template <typename PointT> inline void 00101 setInputCloud (const typename pcl::PointCloud<PointT>::ConstPtr &cloud) 00102 { 00103 if (!data_container_) 00104 data_container_.reset (new DataContainer<PointT>); 00105 boost::static_pointer_cast<DataContainer<PointT> > (data_container_)->setInputCloud (cloud); 00106 } 00107 00112 template <typename PointT> inline void 00113 setInputTarget (const typename pcl::PointCloud<PointT>::ConstPtr &target) 00114 { 00115 if (!data_container_) 00116 data_container_.reset (new DataContainer<PointT>); 00117 boost::static_pointer_cast<DataContainer<PointT> > (data_container_)->setInputTarget (target); 00118 } 00119 00120 protected: 00121 00125 inline void 00126 applyRejection (pcl::Correspondences &correspondences) 00127 { 00128 getRemainingCorrespondences (*input_correspondences_, correspondences); 00129 } 00130 00134 float max_distance_; 00135 00136 class DataContainerInterface 00137 { 00138 public: 00139 virtual double getCorrespondenceScore (int index) = 0; 00140 virtual double getCorrespondenceScore (const pcl::Correspondence &) = 0; 00141 }; 00142 00143 template <typename PointT> 00144 class DataContainer : public DataContainerInterface 00145 { 00146 typedef typename pcl::PointCloud<PointT>::ConstPtr PointCloudConstPtr; 00147 typedef typename pcl::KdTree<PointT>::Ptr KdTreePtr; 00148 00149 public: 00150 00151 DataContainer () : input_ (), target_ () 00152 { 00153 tree_.reset (new pcl::KdTreeFLANN<PointT>); 00154 } 00155 00156 inline void 00157 setInputCloud (const PointCloudConstPtr &cloud) 00158 { 00159 input_ = cloud; 00160 } 00161 00162 inline void 00163 setInputTarget (const PointCloudConstPtr &target) 00164 { 00165 target_ = target; 00166 tree_->setInputCloud (target_); 00167 } 00168 00169 inline double 00170 getCorrespondenceScore (int index) 00171 { 00172 std::vector<int> indices (1); 00173 std::vector<float> distances (1); 00174 if (tree_->nearestKSearch (input_->points[index], 1, indices, distances)) 00175 { 00176 return (distances[0]); 00177 } 00178 else 00179 return (std::numeric_limits<double>::max ()); 00180 } 00181 00182 inline double 00183 getCorrespondenceScore (const pcl::Correspondence &corr) 00184 { 00185 // Get the source and the target feature from the list 00186 const PointT &src = input_->points[corr.index_query]; 00187 const PointT &tgt = target_->points[corr.index_match]; 00188 00189 return ((src.getVector4fMap () - tgt.getVector4fMap ()).squaredNorm ()); 00190 } 00191 00192 private: 00193 PointCloudConstPtr input_, target_; 00194 KdTreePtr tree_; 00195 }; 00196 00197 typedef boost::shared_ptr<DataContainerInterface> DataContainerPtr; 00198 00199 DataContainerPtr data_container_; 00200 }; 00201 00202 } 00203 } 00204 00205 #include "pcl/registration/impl/correspondence_rejection_distance.hpp" 00206 00207 #endif /* PCL_REGISTRATION_CORRESPONDENCE_REJECTION_DISTANCE_H_ */
1.8.0