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_MSAC_H_ 00042 #define PCL_SAMPLE_CONSENSUS_IMPL_MSAC_H_ 00043 00044 #include <pcl/sample_consensus/msac.h> 00045 00046 ////////////////////////////////////////////////////////////////////////// 00047 template <typename PointT> bool 00048 pcl::MEstimatorSampleConsensus<PointT>::computeModel (int debug_verbosity_level) 00049 { 00050 // Warn and exit if no threshold was set 00051 if (threshold_ == std::numeric_limits<double>::max()) 00052 { 00053 PCL_ERROR ("[pcl::MEstimatorSampleConsensus::computeModel] No threshold set!\n"); 00054 return (false); 00055 } 00056 00057 iterations_ = 0; 00058 double d_best_penalty = std::numeric_limits<double>::max(); 00059 double k = 1.0; 00060 00061 std::vector<int> best_model; 00062 std::vector<int> selection; 00063 Eigen::VectorXf model_coefficients; 00064 std::vector<double> distances; 00065 00066 int n_inliers_count = 0; 00067 unsigned skipped_count = 0; 00068 // supress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters! 00069 const unsigned max_skip = max_iterations_ * 10; 00070 00071 // Iterate 00072 while (iterations_ < k && skipped_count < max_skip) 00073 { 00074 // Get X samples which satisfy the model criteria 00075 sac_model_->getSamples (iterations_, selection); 00076 00077 if (selection.empty ()) break; 00078 00079 // Search for inliers in the point cloud for the current plane model M 00080 if (!sac_model_->computeModelCoefficients (selection, model_coefficients)) 00081 { 00082 //iterations_++; 00083 ++ skipped_count; 00084 continue; 00085 } 00086 00087 double d_cur_penalty = 0; 00088 // Iterate through the 3d points and calculate the distances from them to the model 00089 sac_model_->getDistancesToModel (model_coefficients, distances); 00090 00091 if (distances.empty () && k > 1.0) 00092 continue; 00093 00094 for (size_t i = 0; i < distances.size (); ++i) 00095 d_cur_penalty += (std::min) (distances[i], threshold_); 00096 00097 // Better match ? 00098 if (d_cur_penalty < d_best_penalty) 00099 { 00100 d_best_penalty = d_cur_penalty; 00101 00102 // Save the current model/coefficients selection as being the best so far 00103 model_ = selection; 00104 model_coefficients_ = model_coefficients; 00105 00106 n_inliers_count = 0; 00107 // Need to compute the number of inliers for this model to adapt k 00108 for (size_t i = 0; i < distances.size (); ++i) 00109 if (distances[i] <= threshold_) 00110 ++n_inliers_count; 00111 00112 // Compute the k parameter (k=log(z)/log(1-w^n)) 00113 double w = static_cast<double> (n_inliers_count) / static_cast<double> (sac_model_->getIndices ()->size ()); 00114 double p_no_outliers = 1.0 - pow (w, static_cast<double> (selection.size ())); 00115 p_no_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by -Inf 00116 p_no_outliers = (std::min) (1.0 - std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by 0. 00117 k = log (1.0 - probability_) / log (p_no_outliers); 00118 } 00119 00120 ++iterations_; 00121 if (debug_verbosity_level > 1) 00122 PCL_DEBUG ("[pcl::MEstimatorSampleConsensus::computeModel] Trial %d out of %d. Best penalty is %f.\n", iterations_, static_cast<int> (ceil (k)), d_best_penalty); 00123 if (iterations_ > max_iterations_) 00124 { 00125 if (debug_verbosity_level > 0) 00126 PCL_DEBUG ("[pcl::MEstimatorSampleConsensus::computeModel] MSAC reached the maximum number of trials.\n"); 00127 break; 00128 } 00129 } 00130 00131 if (model_.empty ()) 00132 { 00133 if (debug_verbosity_level > 0) 00134 PCL_DEBUG ("[pcl::MEstimatorSampleConsensus::computeModel] Unable to find a solution!\n"); 00135 return (false); 00136 } 00137 00138 // Iterate through the 3d points and calculate the distances from them to the model again 00139 sac_model_->getDistancesToModel (model_coefficients_, distances); 00140 std::vector<int> &indices = *sac_model_->getIndices (); 00141 00142 if (distances.size () != indices.size ()) 00143 { 00144 PCL_ERROR ("[pcl::MEstimatorSampleConsensus::computeModel] Estimated distances (%zu) differs than the normal of indices (%zu).\n", distances.size (), indices.size ()); 00145 return (false); 00146 } 00147 00148 inliers_.resize (distances.size ()); 00149 // Get the inliers for the best model found 00150 n_inliers_count = 0; 00151 for (size_t i = 0; i < distances.size (); ++i) 00152 if (distances[i] <= threshold_) 00153 inliers_[n_inliers_count++] = indices[i]; 00154 00155 // Resize the inliers vector 00156 inliers_.resize (n_inliers_count); 00157 00158 if (debug_verbosity_level > 0) 00159 PCL_DEBUG ("[pcl::MEstimatorSampleConsensus::computeModel] Model: %zu size, %d inliers.\n", model_.size (), n_inliers_count); 00160 00161 return (true); 00162 } 00163 00164 #define PCL_INSTANTIATE_MEstimatorSampleConsensus(T) template class PCL_EXPORTS pcl::MEstimatorSampleConsensus<T>; 00165 00166 #endif // PCL_SAMPLE_CONSENSUS_IMPL_MSAC_H_