Point Cloud Library (PCL)
1.3.1
|
00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Copyright (c) 2009, 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: vfh.hpp 3022 2011-11-01 03:42:22Z rusu $ 00035 * 00036 */ 00037 00038 #ifndef PCL_FEATURES_IMPL_VFH_H_ 00039 #define PCL_FEATURES_IMPL_VFH_H_ 00040 00041 #include "pcl/features/vfh.h" 00042 #include "pcl/features/pfh.h" 00043 #include <pcl/common/common.h> 00044 00046 template<typename PointInT, typename PointNT, typename PointOutT> void 00047 pcl::VFHEstimation<PointInT, PointNT, PointOutT>::computePointSPFHSignature (const Eigen::Vector4f ¢roid_p, 00048 const Eigen::Vector4f ¢roid_n, 00049 const pcl::PointCloud<PointInT> &cloud, 00050 const pcl::PointCloud<PointNT> &normals, 00051 const std::vector<int> &indices) 00052 { 00053 Eigen::Vector4f pfh_tuple; 00054 // Reset the whole thing 00055 hist_f1_.setZero (nr_bins_f1_); 00056 hist_f2_.setZero (nr_bins_f2_); 00057 hist_f3_.setZero (nr_bins_f3_); 00058 hist_f4_.setZero (nr_bins_f4_); 00059 00060 // Get the bounding box of the current cluster 00061 //Eigen::Vector4f min_pt, max_pt; 00062 //pcl::getMinMax3D (cloud, indices, min_pt, max_pt); 00063 //double distance_normalization_factor = (std::max)((centroid_p - min_pt).norm (), (centroid_p - max_pt).norm ()); 00064 00065 //Instead of using the bounding box to normalize the VFH distance component, it is better to use the max_distance 00066 //from any point to centroid. VFH is invariant to rotation about the roll axis but the bounding box is not, 00067 //resulting in different normalization factors for point clouds that are just rotated about that axis. 00068 00069 double distance_normalization_factor = 1.0; 00070 if ( normalize_distances_ ) { 00071 Eigen::Vector4f max_pt; 00072 pcl::getMaxDistance (cloud, indices, centroid_p, max_pt); 00073 max_pt[3] = 0; 00074 distance_normalization_factor = (centroid_p - max_pt).norm (); 00075 } 00076 00077 // Factorization constant 00078 float hist_incr; 00079 if (normalize_bins_) { 00080 hist_incr = 100.0 / (float)(indices.size () - 1); 00081 } else { 00082 hist_incr = 1.0; 00083 } 00084 00085 float hist_incr_size_component; 00086 if (size_component_) 00087 hist_incr_size_component = hist_incr; 00088 else 00089 hist_incr_size_component = 0.0; 00090 00091 // Iterate over all the points in the neighborhood 00092 for (size_t idx = 0; idx < indices.size (); ++idx) 00093 { 00094 // Compute the pair P to NNi 00095 if (!computePairFeatures (centroid_p, centroid_n, cloud.points[indices[idx]].getVector4fMap (), 00096 normals.points[indices[idx]].getNormalVector4fMap (), pfh_tuple[0], pfh_tuple[1], 00097 pfh_tuple[2], pfh_tuple[3])) 00098 continue; 00099 00100 // Normalize the f1, f2, f3, f4 features and push them in the histogram 00101 int h_index = floor (nr_bins_f1_ * ((pfh_tuple[0] + M_PI) * d_pi_)); 00102 if (h_index < 0) 00103 h_index = 0; 00104 if (h_index >= nr_bins_f1_) 00105 h_index = nr_bins_f1_ - 1; 00106 hist_f1_ (h_index) += hist_incr; 00107 00108 h_index = floor (nr_bins_f2_ * ((pfh_tuple[1] + 1.0) * 0.5)); 00109 if (h_index < 0) 00110 h_index = 0; 00111 if (h_index >= nr_bins_f2_) 00112 h_index = nr_bins_f2_ - 1; 00113 hist_f2_ (h_index) += hist_incr; 00114 00115 h_index = floor (nr_bins_f3_ * ((pfh_tuple[2] + 1.0) * 0.5)); 00116 if (h_index < 0) 00117 h_index = 0; 00118 if (h_index >= nr_bins_f3_) 00119 h_index = nr_bins_f3_ - 1; 00120 hist_f3_ (h_index) += hist_incr; 00121 00122 if (normalize_distances_) 00123 h_index = floor (nr_bins_f4_ * (pfh_tuple[3] / distance_normalization_factor)); 00124 else 00125 h_index = pcl_round (pfh_tuple[3] * 100); 00126 00127 if (h_index < 0) 00128 h_index = 0; 00129 if (h_index >= nr_bins_f4_) 00130 h_index = nr_bins_f4_ - 1; 00131 00132 hist_f4_ (h_index) += hist_incr_size_component; 00133 } 00134 } 00136 template <typename PointInT, typename PointNT, typename PointOutT> void 00137 pcl::VFHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output) 00138 { 00139 // ---[ Step 1a : compute the centroid in XYZ space 00140 Eigen::Vector4f xyz_centroid; 00141 00142 if (use_given_centroid_) 00143 xyz_centroid = centroid_to_use_; 00144 else 00145 compute3DCentroid (*surface_, *indices_, xyz_centroid); // Estimate the XYZ centroid 00146 00147 // ---[ Step 1b : compute the centroid in normal space 00148 Eigen::Vector4f normal_centroid = Eigen::Vector4f::Zero (); 00149 int cp = 0; 00150 00151 // If the data is dense, we don't need to check for NaN 00152 00153 if (use_given_normal_) 00154 normal_centroid = normal_to_use_; 00155 else 00156 { 00157 if (normals_->is_dense) 00158 { 00159 for (size_t i = 0; i < indices_->size (); ++i) 00160 { 00161 normal_centroid += normals_->points[(*indices_)[i]].getNormalVector4fMap (); 00162 cp++; 00163 } 00164 } 00165 // NaN or Inf values could exist => check for them 00166 else 00167 { 00168 for (size_t i = 0; i < indices_->size (); ++i) 00169 { 00170 if (!pcl_isfinite (normals_->points[(*indices_)[i]].normal[0]) 00171 || 00172 !pcl_isfinite (normals_->points[(*indices_)[i]].normal[1]) 00173 || 00174 !pcl_isfinite (normals_->points[(*indices_)[i]].normal[2])) 00175 continue; 00176 normal_centroid += normals_->points[(*indices_)[i]].getNormalVector4fMap (); 00177 cp++; 00178 } 00179 } 00180 normal_centroid /= cp; 00181 } 00182 00183 // Compute the direction of view from the viewpoint to the centroid 00184 Eigen::Vector4f viewpoint (vpx_, vpy_, vpz_, 0); 00185 Eigen::Vector4f d_vp_p = viewpoint - xyz_centroid; 00186 d_vp_p.normalize (); 00187 00188 // Estimate the SPFH at nn_indices[0] using the entire cloud 00189 computePointSPFHSignature (xyz_centroid, normal_centroid, *surface_, *normals_, *indices_); 00190 00191 // We only output _1_ signature 00192 output.points.resize (1); 00193 output.width = 1; 00194 output.height = 1; 00195 00196 // Estimate the FPFH at nn_indices[0] using the entire cloud and copy the resultant signature 00197 for (int d = 0; d < hist_f1_.size (); ++d) 00198 output.points[0].histogram[d + 0] = hist_f1_[d]; 00199 00200 size_t data_size = hist_f1_.size (); 00201 for (int d = 0; d < hist_f2_.size (); ++d) 00202 output.points[0].histogram[d + data_size] = hist_f2_[d]; 00203 00204 data_size += hist_f2_.size (); 00205 for (int d = 0; d < hist_f3_.size (); ++d) 00206 output.points[0].histogram[d + data_size] = hist_f3_[d]; 00207 00208 data_size += hist_f3_.size (); 00209 for (int d = 0; d < hist_f4_.size (); ++d) 00210 output.points[0].histogram[d + data_size] = hist_f4_[d]; 00211 00212 // ---[ Step 2 : obtain the viewpoint component 00213 hist_vp_.setZero (nr_bins_vp_); 00214 00215 double hist_incr; 00216 if (normalize_bins_) 00217 hist_incr = 100.0 / (double)(indices_->size ()); 00218 else 00219 hist_incr = 1.0; 00220 00221 for (size_t i = 0; i < indices_->size (); ++i) 00222 { 00223 Eigen::Vector4f normal (normals_->points[(*indices_)[i]].normal[0], 00224 normals_->points[(*indices_)[i]].normal[1], 00225 normals_->points[(*indices_)[i]].normal[2], 0); 00226 // Normalize 00227 double alpha = (normal.dot (d_vp_p) + 1.0) * 0.5; 00228 int fi = floor (alpha * hist_vp_.size ()); 00229 if (fi < 0) 00230 fi = 0; 00231 if (fi > ((int)hist_vp_.size () - 1)) 00232 fi = hist_vp_.size () - 1; 00233 // Bin into the histogram 00234 hist_vp_ [fi] += hist_incr; 00235 } 00236 data_size += hist_f4_.size (); 00237 // Copy the resultant signature 00238 for (int d = 0; d < hist_vp_.size (); ++d) 00239 output.points[0].histogram[d + data_size] = hist_vp_[d]; 00240 } 00241 00242 #define PCL_INSTANTIATE_VFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::VFHEstimation<T,NT,OutT>; 00243 00244 #endif // PCL_FEATURES_IMPL_VFH_H_