Point Cloud Library (PCL)
1.7.0
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00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Point Cloud Library (PCL) - www.pointclouds.org 00005 * Copyright (c) 2010-2011, 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_FEATURES_CVFH_H_ 00042 #define PCL_FEATURES_CVFH_H_ 00043 00044 #include <pcl/features/feature.h> 00045 #include <pcl/features/vfh.h> 00046 #include <pcl/search/pcl_search.h> 00047 #include <pcl/common/common.h> 00048 00049 namespace pcl 00050 { 00051 /** \brief CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given 00052 * point cloud dataset containing XYZ data and normals, as presented in: 00053 * - CAD-Model Recognition and 6 DOF Pose Estimation 00054 * A. Aldoma, N. Blodow, D. Gossow, S. Gedikli, R.B. Rusu, M. Vincze and G. Bradski 00055 * ICCV 2011, 3D Representation and Recognition (3dRR11) workshop 00056 * Barcelona, Spain, (2011) 00057 * 00058 * The suggested PointOutT is pcl::VFHSignature308. 00059 * 00060 * \author Aitor Aldoma 00061 * \ingroup features 00062 */ 00063 template<typename PointInT, typename PointNT, typename PointOutT = pcl::VFHSignature308> 00064 class CVFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT> 00065 { 00066 public: 00067 typedef boost::shared_ptr<CVFHEstimation<PointInT, PointNT, PointOutT> > Ptr; 00068 typedef boost::shared_ptr<const CVFHEstimation<PointInT, PointNT, PointOutT> > ConstPtr; 00069 00070 using Feature<PointInT, PointOutT>::feature_name_; 00071 using Feature<PointInT, PointOutT>::getClassName; 00072 using Feature<PointInT, PointOutT>::indices_; 00073 using Feature<PointInT, PointOutT>::k_; 00074 using Feature<PointInT, PointOutT>::search_radius_; 00075 using Feature<PointInT, PointOutT>::surface_; 00076 using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_; 00077 00078 typedef typename Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut; 00079 typedef typename pcl::search::Search<PointNormal>::Ptr KdTreePtr; 00080 typedef typename pcl::VFHEstimation<PointInT, PointNT, pcl::VFHSignature308> VFHEstimator; 00081 00082 /** \brief Empty constructor. */ 00083 CVFHEstimation () : 00084 vpx_ (0), vpy_ (0), vpz_ (0), 00085 leaf_size_ (0.005f), 00086 normalize_bins_ (false), 00087 curv_threshold_ (0.03f), 00088 cluster_tolerance_ (leaf_size_ * 3), 00089 eps_angle_threshold_ (0.125f), 00090 min_points_ (50), 00091 radius_normals_ (leaf_size_ * 3), 00092 centroids_dominant_orientations_ (), 00093 dominant_normals_ () 00094 { 00095 search_radius_ = 0; 00096 k_ = 1; 00097 feature_name_ = "CVFHEstimation"; 00098 } 00099 ; 00100 00101 /** \brief Removes normals with high curvature caused by real edges or noisy data 00102 * \param[in] cloud pointcloud to be filtered 00103 * \param[out] indices_out the indices of the points with higher curvature than threshold 00104 * \param[out] indices_in the indices of the remaining points after filtering 00105 * \param[in] threshold threshold value for curvature 00106 */ 00107 void 00108 filterNormalsWithHighCurvature (const pcl::PointCloud<PointNT> & cloud, std::vector<int> & indices_to_use, std::vector<int> &indices_out, 00109 std::vector<int> &indices_in, float threshold); 00110 00111 /** \brief Set the viewpoint. 00112 * \param[in] vpx the X coordinate of the viewpoint 00113 * \param[in] vpy the Y coordinate of the viewpoint 00114 * \param[in] vpz the Z coordinate of the viewpoint 00115 */ 00116 inline void 00117 setViewPoint (float vpx, float vpy, float vpz) 00118 { 00119 vpx_ = vpx; 00120 vpy_ = vpy; 00121 vpz_ = vpz; 00122 } 00123 00124 /** \brief Set the radius used to compute normals 00125 * \param[in] radius_normals the radius 00126 */ 00127 inline void 00128 setRadiusNormals (float radius_normals) 00129 { 00130 radius_normals_ = radius_normals; 00131 } 00132 00133 /** \brief Get the viewpoint. 00134 * \param[out] vpx the X coordinate of the viewpoint 00135 * \param[out] vpy the Y coordinate of the viewpoint 00136 * \param[out] vpz the Z coordinate of the viewpoint 00137 */ 00138 inline void 00139 getViewPoint (float &vpx, float &vpy, float &vpz) 00140 { 00141 vpx = vpx_; 00142 vpy = vpy_; 00143 vpz = vpz_; 00144 } 00145 00146 /** \brief Get the centroids used to compute different CVFH descriptors 00147 * \param[out] centroids vector to hold the centroids 00148 */ 00149 inline void 00150 getCentroidClusters (std::vector<Eigen::Vector3f> & centroids) 00151 { 00152 for (size_t i = 0; i < centroids_dominant_orientations_.size (); ++i) 00153 centroids.push_back (centroids_dominant_orientations_[i]); 00154 } 00155 00156 /** \brief Get the normal centroids used to compute different CVFH descriptors 00157 * \param[out] centroids vector to hold the normal centroids 00158 */ 00159 inline void 00160 getCentroidNormalClusters (std::vector<Eigen::Vector3f> & centroids) 00161 { 00162 for (size_t i = 0; i < dominant_normals_.size (); ++i) 00163 centroids.push_back (dominant_normals_[i]); 00164 } 00165 00166 /** \brief Sets max. Euclidean distance between points to be added to the cluster 00167 * \param[in] d the maximum Euclidean distance 00168 */ 00169 00170 inline void 00171 setClusterTolerance (float d) 00172 { 00173 cluster_tolerance_ = d; 00174 } 00175 00176 /** \brief Sets max. deviation of the normals between two points so they can be clustered together 00177 * \param[in] d the maximum deviation 00178 */ 00179 inline void 00180 setEPSAngleThreshold (float d) 00181 { 00182 eps_angle_threshold_ = d; 00183 } 00184 00185 /** \brief Sets curvature threshold for removing normals 00186 * \param[in] d the curvature threshold 00187 */ 00188 inline void 00189 setCurvatureThreshold (float d) 00190 { 00191 curv_threshold_ = d; 00192 } 00193 00194 /** \brief Set minimum amount of points for a cluster to be considered 00195 * \param[in] min the minimum amount of points to be set 00196 */ 00197 inline void 00198 setMinPoints (size_t min) 00199 { 00200 min_points_ = min; 00201 } 00202 00203 /** \brief Sets wether if the CVFH signatures should be normalized or not 00204 * \param[in] normalize true if normalization is required, false otherwise 00205 */ 00206 inline void 00207 setNormalizeBins (bool normalize) 00208 { 00209 normalize_bins_ = normalize; 00210 } 00211 00212 /** \brief Overloaded computed method from pcl::Feature. 00213 * \param[out] output the resultant point cloud model dataset containing the estimated features 00214 */ 00215 void 00216 compute (PointCloudOut &output); 00217 00218 private: 00219 /** \brief Values describing the viewpoint ("pinhole" camera model assumed). 00220 * By default, the viewpoint is set to 0,0,0. 00221 */ 00222 float vpx_, vpy_, vpz_; 00223 00224 /** \brief Size of the voxels after voxel gridding. IMPORTANT: Must match the voxel 00225 * size of the training data or the normalize_bins_ flag must be set to true. 00226 */ 00227 float leaf_size_; 00228 00229 /** \brief Wether to normalize the signatures or not. Default: false. */ 00230 bool normalize_bins_; 00231 00232 /** \brief Curvature threshold for removing normals. */ 00233 float curv_threshold_; 00234 00235 /** \brief allowed Euclidean distance between points to be added to the cluster. */ 00236 float cluster_tolerance_; 00237 00238 /** \brief deviation of the normals between two points so they can be clustered together. */ 00239 float eps_angle_threshold_; 00240 00241 /** \brief Minimum amount of points in a clustered region to be considered stable for CVFH 00242 * computation. 00243 */ 00244 size_t min_points_; 00245 00246 /** \brief Radius for the normals computation. */ 00247 float radius_normals_; 00248 00249 /** \brief Estimate the Clustered Viewpoint Feature Histograms (CVFH) descriptors at 00250 * a set of points given by <setInputCloud (), setIndices ()> using the surface in 00251 * setSearchSurface () 00252 * 00253 * \param[out] output the resultant point cloud model dataset that contains the CVFH 00254 * feature estimates 00255 */ 00256 void 00257 computeFeature (PointCloudOut &output); 00258 00259 /** \brief Region growing method using Euclidean distances and neighbors normals to 00260 * add points to a region. 00261 * \param[in] cloud point cloud to split into regions 00262 * \param[in] normals are the normals of cloud 00263 * \param[in] tolerance is the allowed Euclidean distance between points to be added to 00264 * the cluster 00265 * \param[in] tree is the spatial search structure for nearest neighbour search 00266 * \param[out] clusters vector of indices representing the clustered regions 00267 * \param[in] eps_angle deviation of the normals between two points so they can be 00268 * clustered together 00269 * \param[in] min_pts_per_cluster minimum cluster size. (default: 1 point) 00270 * \param[in] max_pts_per_cluster maximum cluster size. (default: all the points) 00271 */ 00272 void 00273 extractEuclideanClustersSmooth (const pcl::PointCloud<pcl::PointNormal> &cloud, 00274 const pcl::PointCloud<pcl::PointNormal> &normals, float tolerance, 00275 const pcl::search::Search<pcl::PointNormal>::Ptr &tree, 00276 std::vector<pcl::PointIndices> &clusters, double eps_angle, 00277 unsigned int min_pts_per_cluster = 1, 00278 unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ()); 00279 00280 protected: 00281 /** \brief Centroids that were used to compute different CVFH descriptors */ 00282 std::vector<Eigen::Vector3f> centroids_dominant_orientations_; 00283 /** \brief Normal centroids that were used to compute different CVFH descriptors */ 00284 std::vector<Eigen::Vector3f> dominant_normals_; 00285 }; 00286 } 00287 00288 #ifdef PCL_NO_PRECOMPILE 00289 #include <pcl/features/impl/cvfh.hpp> 00290 #endif 00291 00292 #endif //#ifndef PCL_FEATURES_CVFH_H_