Point Cloud Library (PCL)
1.3.1
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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: rmsac.hpp 3280 2011-11-30 18:31:35Z gedikli $ 00035 * 00036 */ 00037 00038 #ifndef PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_ 00039 #define PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_ 00040 00041 #include "pcl/sample_consensus/rmsac.h" 00042 00044 template <typename PointT> bool 00045 pcl::RandomizedMEstimatorSampleConsensus<PointT>::computeModel (int debug_verbosity_level) 00046 { 00047 // Warn and exit if no threshold was set 00048 if (threshold_ == DBL_MAX) 00049 { 00050 PCL_ERROR ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] No threshold set!\n"); 00051 return (false); 00052 } 00053 00054 iterations_ = 0; 00055 double d_best_penalty = DBL_MAX; 00056 double k = 1.0; 00057 00058 std::vector<int> best_model; 00059 std::vector<int> selection; 00060 Eigen::VectorXf model_coefficients; 00061 std::vector<double> distances; 00062 std::set<int> indices_subset; 00063 00064 int n_inliers_count = 0; 00065 unsigned skipped_count = 0; 00066 // supress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters! 00067 const unsigned max_skip = max_iterations_ * 10; 00068 00069 // Number of samples to try randomly 00070 size_t fraction_nr_points = pcl_lrint (sac_model_->getIndices ()->size () * fraction_nr_pretest_ / 100.0); 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 ()) break; 00079 00080 // Search for inliers in the point cloud for the current plane model M 00081 if (!sac_model_->computeModelCoefficients (selection, model_coefficients)) 00082 { 00083 //iterations_++; 00084 ++ skipped_count; 00085 continue; 00086 } 00087 00088 // RMSAC addon: verify a random fraction of the data 00089 // Get X random samples which satisfy the model criterion 00090 this->getRandomSamples (sac_model_->getIndices (), fraction_nr_points, indices_subset); 00091 00092 if (!sac_model_->doSamplesVerifyModel (indices_subset, model_coefficients, threshold_)) 00093 { 00094 // Unfortunately we cannot "continue" after the first iteration, because k might not be set, while iterations gets incremented 00095 if (k != 1.0) 00096 { 00097 ++iterations_; 00098 continue; 00099 } 00100 } 00101 00102 double d_cur_penalty = 0; 00103 // Iterate through the 3d points and calculate the distances from them to the model 00104 sac_model_->getDistancesToModel (model_coefficients, distances); 00105 00106 if (distances.empty () && k > 1.0) 00107 continue; 00108 00109 for (size_t i = 0; i < distances.size (); ++i) 00110 d_cur_penalty += (std::min) (distances[i], threshold_); 00111 00112 // Better match ? 00113 if (d_cur_penalty < d_best_penalty) 00114 { 00115 d_best_penalty = d_cur_penalty; 00116 00117 // Save the current model/coefficients selection as being the best so far 00118 model_ = selection; 00119 model_coefficients_ = model_coefficients; 00120 00121 n_inliers_count = 0; 00122 // Need to compute the number of inliers for this model to adapt k 00123 for (size_t i = 0; i < distances.size (); ++i) 00124 if (distances[i] <= threshold_) 00125 n_inliers_count++; 00126 00127 // Compute the k parameter (k=log(z)/log(1-w^n)) 00128 double w = (double)((double)n_inliers_count / (double)sac_model_->getIndices ()->size ()); 00129 double p_no_outliers = 1 - pow (w, (double)selection.size ()); 00130 p_no_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by -Inf 00131 p_no_outliers = (std::min) (1 - std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by 0. 00132 k = log (1 - probability_) / log (p_no_outliers); 00133 } 00134 00135 ++iterations_; 00136 if (debug_verbosity_level > 1) 00137 PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Trial %d out of %d. Best penalty is %f.\n", iterations_, (int)ceil (k), d_best_penalty); 00138 if (iterations_ > max_iterations_) 00139 { 00140 if (debug_verbosity_level > 0) 00141 PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] MSAC reached the maximum number of trials.\n"); 00142 break; 00143 } 00144 } 00145 00146 if (model_.empty ()) 00147 { 00148 if (debug_verbosity_level > 0) 00149 PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Unable to find a solution!\n"); 00150 return (false); 00151 } 00152 00153 // Iterate through the 3d points and calculate the distances from them to the model again 00154 sac_model_->getDistancesToModel (model_coefficients_, distances); 00155 std::vector<int> &indices = *sac_model_->getIndices (); 00156 if (distances.size () != indices.size ()) 00157 { 00158 PCL_ERROR ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Estimated distances (%lu) differs than the normal of indices (%lu).\n", (unsigned long)distances.size (), (unsigned long)indices.size ()); 00159 return (false); 00160 } 00161 00162 inliers_.resize (distances.size ()); 00163 // Get the inliers for the best model found 00164 n_inliers_count = 0; 00165 for (size_t i = 0; i < distances.size (); ++i) 00166 if (distances[i] <= threshold_) 00167 inliers_[n_inliers_count++] = indices[i]; 00168 00169 // Resize the inliers vector 00170 inliers_.resize (n_inliers_count); 00171 00172 if (debug_verbosity_level > 0) 00173 PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Model: %lu size, %d inliers.\n", (unsigned long)model_.size (), n_inliers_count); 00174 00175 return (true); 00176 } 00177 00178 #define PCL_INSTANTIATE_RandomizedMEstimatorSampleConsensus(T) template class PCL_EXPORTS pcl::RandomizedMEstimatorSampleConsensus<T>; 00179 00180 #endif // PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_ 00181