Point Cloud Library (PCL)
1.7.0
|
00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Point Cloud Library (PCL) - www.pointclouds.org 00005 * Copyright (c) 2009, 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 00041 #ifndef PCL_SAMPLE_CONSENSUS_IMPL_RANSAC_H_ 00042 #define PCL_SAMPLE_CONSENSUS_IMPL_RANSAC_H_ 00043 00044 #include <pcl/sample_consensus/ransac.h> 00045 00046 ////////////////////////////////////////////////////////////////////////// 00047 template <typename PointT> bool 00048 pcl::RandomSampleConsensus<PointT>::computeModel (int) 00049 { 00050 // Warn and exit if no threshold was set 00051 if (threshold_ == std::numeric_limits<double>::max()) 00052 { 00053 PCL_ERROR ("[pcl::RandomSampleConsensus::computeModel] No threshold set!\n"); 00054 return (false); 00055 } 00056 00057 iterations_ = 0; 00058 int n_best_inliers_count = -INT_MAX; 00059 double k = 1.0; 00060 00061 std::vector<int> selection; 00062 Eigen::VectorXf model_coefficients; 00063 00064 double log_probability = log (1.0 - probability_); 00065 double one_over_indices = 1.0 / static_cast<double> (sac_model_->getIndices ()->size ()); 00066 00067 int n_inliers_count = 0; 00068 unsigned skipped_count = 0; 00069 // supress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters! 00070 const unsigned max_skip = max_iterations_ * 10; 00071 00072 // Iterate 00073 while (iterations_ < k && skipped_count < max_skip) 00074 { 00075 // Get X samples which satisfy the model criteria 00076 sac_model_->getSamples (iterations_, selection); 00077 00078 if (selection.empty ()) 00079 { 00080 PCL_ERROR ("[pcl::RandomSampleConsensus::computeModel] No samples could be selected!\n"); 00081 break; 00082 } 00083 00084 // Search for inliers in the point cloud for the current plane model M 00085 if (!sac_model_->computeModelCoefficients (selection, model_coefficients)) 00086 { 00087 //++iterations_; 00088 ++skipped_count; 00089 continue; 00090 } 00091 00092 // Select the inliers that are within threshold_ from the model 00093 //sac_model_->selectWithinDistance (model_coefficients, threshold_, inliers); 00094 //if (inliers.empty () && k > 1.0) 00095 // continue; 00096 00097 n_inliers_count = sac_model_->countWithinDistance (model_coefficients, threshold_); 00098 00099 // Better match ? 00100 if (n_inliers_count > n_best_inliers_count) 00101 { 00102 n_best_inliers_count = n_inliers_count; 00103 00104 // Save the current model/inlier/coefficients selection as being the best so far 00105 model_ = selection; 00106 model_coefficients_ = model_coefficients; 00107 00108 // Compute the k parameter (k=log(z)/log(1-w^n)) 00109 double w = static_cast<double> (n_best_inliers_count) * one_over_indices; 00110 double p_no_outliers = 1.0 - pow (w, static_cast<double> (selection.size ())); 00111 p_no_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by -Inf 00112 p_no_outliers = (std::min) (1.0 - std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by 0. 00113 k = log_probability / log (p_no_outliers); 00114 } 00115 00116 ++iterations_; 00117 PCL_DEBUG ("[pcl::RandomSampleConsensus::computeModel] Trial %d out of %f: %d inliers (best is: %d so far).\n", iterations_, k, n_inliers_count, n_best_inliers_count); 00118 if (iterations_ > max_iterations_) 00119 { 00120 PCL_DEBUG ("[pcl::RandomSampleConsensus::computeModel] RANSAC reached the maximum number of trials.\n"); 00121 break; 00122 } 00123 } 00124 00125 PCL_DEBUG ("[pcl::RandomSampleConsensus::computeModel] Model: %zu size, %d inliers.\n", model_.size (), n_best_inliers_count); 00126 00127 if (model_.empty ()) 00128 { 00129 inliers_.clear (); 00130 return (false); 00131 } 00132 00133 // Get the set of inliers that correspond to the best model found so far 00134 sac_model_->selectWithinDistance (model_coefficients_, threshold_, inliers_); 00135 return (true); 00136 } 00137 00138 #define PCL_INSTANTIATE_RandomSampleConsensus(T) template class PCL_EXPORTS pcl::RandomSampleConsensus<T>; 00139 00140 #endif // PCL_SAMPLE_CONSENSUS_IMPL_RANSAC_H_ 00141