Point Cloud Library (PCL)
1.3.1
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00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Copyright (c) 2010, Willow Garage, Inc. 00005 * All rights reserved. 00006 * 00007 * Redistribution and use in source and binary forms, with or without 00008 * modification, are permitted provided that the following conditions 00009 * are met: 00010 * 00011 * * Redistributions of source code must retain the above copyright 00012 * notice, this list of conditions and the following disclaimer. 00013 * * Redistributions in binary form must reproduce the above 00014 * copyright notice, this list of conditions and the following 00015 * disclaimer in the documentation and/or other materials provided 00016 * with the distribution. 00017 * * Neither the name of Willow Garage, Inc. nor the names of its 00018 * contributors may be used to endorse or promote products derived 00019 * from this software without specific prior written permission. 00020 * 00021 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 00022 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 00023 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 00024 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 00025 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00026 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00027 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00028 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00029 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00030 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00031 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00032 * POSSIBILITY OF SUCH DAMAGE. 00033 * 00034 * $Id: ia_ransac.hpp 3041 2011-11-01 04:44:41Z rusu $ 00035 * 00036 */ 00037 00039 template <typename PointSource, typename PointTarget, typename FeatureT> void 00040 pcl::SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT>::setSourceFeatures (const FeatureCloudConstPtr &features) 00041 { 00042 if (features == NULL || features->points.empty ()) 00043 { 00044 PCL_ERROR ("[pcl::%s::setSourceFeatures] Invalid or empty point cloud dataset given!\n", getClassName ().c_str ()); 00045 return; 00046 } 00047 input_features_ = features; 00048 } 00049 00051 template <typename PointSource, typename PointTarget, typename FeatureT> void 00052 pcl::SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT>::setTargetFeatures (const FeatureCloudConstPtr &features) 00053 { 00054 if (features == NULL || features->points.empty ()) 00055 { 00056 PCL_ERROR ("[pcl::%s::setTargetFeatures] Invalid or empty point cloud dataset given!\n", getClassName ().c_str ()); 00057 return; 00058 } 00059 target_features_ = features; 00060 feature_tree_->setInputCloud (target_features_); 00061 } 00062 00064 template <typename PointSource, typename PointTarget, typename FeatureT> void 00065 pcl::SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT>::selectSamples ( 00066 const PointCloudSource &cloud, int nr_samples, float min_sample_distance, 00067 std::vector<int> &sample_indices) 00068 { 00069 if (nr_samples > (int) cloud.points.size ()) 00070 { 00071 PCL_ERROR ("[pcl::%s::selectSamples] ", getClassName ().c_str ()); 00072 PCL_ERROR ("The number of samples (%d) must not be greater than the number of points (%d)!\n", 00073 nr_samples, (int) cloud.points.size ()); 00074 return; 00075 } 00076 00077 // Iteratively draw random samples until nr_samples is reached 00078 int iterations_without_a_sample = 0; 00079 int max_iterations_without_a_sample = (int) (3 * cloud.points.size ()); 00080 sample_indices.clear (); 00081 while ((int) sample_indices.size () < nr_samples) 00082 { 00083 // Choose a sample at random 00084 int sample_index = getRandomIndex ((int) cloud.points.size ()); 00085 00086 // Check to see if the sample is 1) unique and 2) far away from the other samples 00087 bool valid_sample = true; 00088 for (size_t i = 0; i < sample_indices.size (); ++i) 00089 { 00090 float distance_between_samples = euclideanDistance (cloud.points[sample_index], cloud.points[sample_indices[i]]); 00091 00092 if (sample_index == sample_indices[i] || distance_between_samples < min_sample_distance) 00093 { 00094 valid_sample = false; 00095 break; 00096 } 00097 } 00098 00099 // If the sample is valid, add it to the output 00100 if (valid_sample) 00101 { 00102 sample_indices.push_back (sample_index); 00103 iterations_without_a_sample = 0; 00104 } 00105 else 00106 { 00107 ++iterations_without_a_sample; 00108 } 00109 00110 // If no valid samples can be found, relax the inter-sample distance requirements 00111 if (iterations_without_a_sample >= max_iterations_without_a_sample) 00112 { 00113 PCL_WARN ("[pcl::%s::selectSamples] ", getClassName ().c_str ()); 00114 PCL_WARN ("No valid sample found after %d iterations. Relaxing min_sample_distance_ to %f\n", 00115 iterations_without_a_sample, 0.5*min_sample_distance); 00116 00117 min_sample_distance_ *= 0.5; 00118 min_sample_distance = min_sample_distance_; 00119 iterations_without_a_sample = 0; 00120 } 00121 } 00122 00123 } 00124 00126 template <typename PointSource, typename PointTarget, typename FeatureT> void 00127 pcl::SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT>::findSimilarFeatures ( 00128 const FeatureCloud &input_features, const std::vector<int> &sample_indices, 00129 std::vector<int> &corresponding_indices) 00130 { 00131 std::vector<int> nn_indices (k_correspondences_); 00132 std::vector<float> nn_distances (k_correspondences_); 00133 00134 corresponding_indices.resize (sample_indices.size ()); 00135 for (size_t i = 0; i < sample_indices.size (); ++i) 00136 { 00137 // Find the k features nearest to input_features.points[sample_indices[i]] 00138 feature_tree_->nearestKSearch (input_features, sample_indices[i], k_correspondences_, nn_indices, nn_distances); 00139 00140 // Select one at random and add it to corresponding_indices 00141 int random_correspondence = getRandomIndex (k_correspondences_); 00142 corresponding_indices[i] = nn_indices[random_correspondence]; 00143 } 00144 } 00145 00147 template <typename PointSource, typename PointTarget, typename FeatureT> float 00148 pcl::SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT>::computeErrorMetric ( 00149 const PointCloudSource &cloud, float threshold) 00150 { 00151 std::vector<int> nn_index (1); 00152 std::vector<float> nn_distance (1); 00153 00154 const ErrorFunctor & compute_error = *error_functor_; 00155 float error = 0; 00156 00157 for (size_t i = 0; i < cloud.points.size (); ++i) 00158 { 00159 // Find the distance between cloud.points[i] and its nearest neighbor in the target point cloud 00160 tree_->nearestKSearch (cloud, (int) i, 1, nn_index, nn_distance); 00161 00162 // Compute the error 00163 error += compute_error (nn_distance[0]); 00164 } 00165 return (error); 00166 } 00167 00169 template <typename PointSource, typename PointTarget, typename FeatureT> void 00170 pcl::SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT>::computeTransformation (PointCloudSource &output) 00171 { 00172 if (!input_features_) 00173 { 00174 PCL_ERROR ("[pcl::%s::computeTransformation] ", getClassName ().c_str ()); 00175 PCL_ERROR ("No source features were given! Call setSourceFeatures before aligning.\n"); 00176 return; 00177 } 00178 if (!target_features_) 00179 { 00180 PCL_ERROR ("[pcl::%s::computeTransformation] ", getClassName ().c_str ()); 00181 PCL_ERROR ("No target features were given! Call setTargetFeatures before aligning.\n"); 00182 return; 00183 } 00184 00185 if (!error_functor_) 00186 { 00187 error_functor_.reset (new TruncatedError (min_sample_distance_)); 00188 } 00189 00190 std::vector<int> sample_indices (nr_samples_); 00191 std::vector<int> corresponding_indices (nr_samples_); 00192 PointCloudSource input_transformed; 00193 float error, lowest_error (0); 00194 00195 final_transformation_ = Eigen::Matrix4f::Identity (); 00196 00197 for (int i_iter = 0; i_iter < max_iterations_; ++i_iter) 00198 { 00199 // Draw nr_samples_ random samples 00200 selectSamples (*input_, nr_samples_, min_sample_distance_, sample_indices); 00201 00202 // Find corresponding features in the target cloud 00203 findSimilarFeatures (*input_features_, sample_indices, corresponding_indices); 00204 00205 // Estimate the transform from the samples to their corresponding points 00206 transformation_estimation_->estimateRigidTransformation (*input_, sample_indices, *target_, corresponding_indices, transformation_); 00207 00208 // Tranform the data and compute the error 00209 transformPointCloud (*input_, input_transformed, transformation_); 00210 error = computeErrorMetric (input_transformed, (float) corr_dist_threshold_); 00211 00212 // If the new error is lower, update the final transformation 00213 if (i_iter == 0 || error < lowest_error) 00214 { 00215 lowest_error = error; 00216 final_transformation_ = transformation_; 00217 } 00218 } 00219 00220 // Apply the final transformation 00221 transformPointCloud (*input_, output, final_transformation_); 00222 }