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) 2010-2011, 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 * $Id$ 00038 * 00039 */ 00040 #ifndef PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_ 00041 #define PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_ 00042 00043 ///////////////////////////////////////////////////////////////////////////////////////////////////////// 00044 template <typename PointSource, typename PointTarget, typename Scalar> double 00045 pcl::registration::TransformationValidationEuclidean<PointSource, PointTarget, Scalar>::validateTransformation ( 00046 const PointCloudSourceConstPtr &cloud_src, 00047 const PointCloudTargetConstPtr &cloud_tgt, 00048 const Matrix4 &transformation_matrix) const 00049 { 00050 double fitness_score = 0.0; 00051 00052 // Transform the input dataset using the final transformation 00053 pcl::PointCloud<PointSource> input_transformed; 00054 //transformPointCloud (*cloud_src, input_transformed, transformation_matrix); 00055 input_transformed.resize (cloud_src->size ()); 00056 for (size_t i = 0; i < cloud_src->size (); ++i) 00057 { 00058 const PointSource &src = cloud_src->points[i]; 00059 PointTarget &tgt = input_transformed.points[i]; 00060 tgt.x = static_cast<float> (transformation_matrix (0, 0) * src.x + transformation_matrix (0, 1) * src.y + transformation_matrix (0, 2) * src.z + transformation_matrix (0, 3)); 00061 tgt.y = static_cast<float> (transformation_matrix (1, 0) * src.x + transformation_matrix (1, 1) * src.y + transformation_matrix (1, 2) * src.z + transformation_matrix (1, 3)); 00062 tgt.z = static_cast<float> (transformation_matrix (2, 0) * src.x + transformation_matrix (2, 1) * src.y + transformation_matrix (2, 2) * src.z + transformation_matrix (2, 3)); 00063 } 00064 00065 typename MyPointRepresentation::ConstPtr point_rep (new MyPointRepresentation); 00066 if (!force_no_recompute_) 00067 { 00068 tree_->setPointRepresentation (point_rep); 00069 tree_->setInputCloud (cloud_tgt); 00070 } 00071 00072 std::vector<int> nn_indices (1); 00073 std::vector<float> nn_dists (1); 00074 00075 // For each point in the source dataset 00076 int nr = 0; 00077 for (size_t i = 0; i < input_transformed.points.size (); ++i) 00078 { 00079 // Find its nearest neighbor in the target 00080 tree_->nearestKSearch (input_transformed.points[i], 1, nn_indices, nn_dists); 00081 00082 // Deal with occlusions (incomplete targets) 00083 if (nn_dists[0] > max_range_) 00084 continue; 00085 00086 // Calculate the fitness score 00087 fitness_score += nn_dists[0]; 00088 ++nr; 00089 } 00090 00091 if (nr > 0) 00092 return (fitness_score / nr); 00093 else 00094 return (std::numeric_limits<double>::max ()); 00095 } 00096 00097 #endif // PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_ 00098