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_FPFH_H_ 00042 #define PCL_FPFH_H_ 00043 00044 #include <pcl/features/feature.h> 00045 #include <set> 00046 00047 namespace pcl 00048 { 00049 /** \brief FPFHEstimation estimates the <b>Fast Point Feature Histogram (FPFH)</b> descriptor for a given point 00050 * cloud dataset containing points and normals. 00051 * 00052 * A commonly used type for PointOutT is pcl::FPFHSignature33. 00053 * 00054 * \note If you use this code in any academic work, please cite: 00055 * 00056 * - R.B. Rusu, N. Blodow, M. Beetz. 00057 * Fast Point Feature Histograms (FPFH) for 3D Registration. 00058 * In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 00059 * Kobe, Japan, May 12-17 2009. 00060 * - R.B. Rusu, A. Holzbach, N. Blodow, M. Beetz. 00061 * Fast Geometric Point Labeling using Conditional Random Fields. 00062 * In Proceedings of the 22nd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 00063 * St. Louis, MO, USA, October 11-15 2009. 00064 * 00065 * \attention 00066 * The convention for FPFH features is: 00067 * - if a query point's nearest neighbors cannot be estimated, the FPFH feature will be set to NaN 00068 * (not a number) 00069 * - it is impossible to estimate a FPFH descriptor for a point that 00070 * doesn't have finite 3D coordinates. Therefore, any point that contains 00071 * NaN data on x, y, or z, will have its FPFH feature property set to NaN. 00072 * 00073 * \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at 00074 * \ref FPFHEstimationOMP for examples on parallel implementations of the FPFH (Fast Point Feature Histogram). 00075 * 00076 * \author Radu B. Rusu 00077 * \ingroup features 00078 */ 00079 template <typename PointInT, typename PointNT, typename PointOutT = pcl::FPFHSignature33> 00080 class FPFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT> 00081 { 00082 public: 00083 typedef boost::shared_ptr<FPFHEstimation<PointInT, PointNT, PointOutT> > Ptr; 00084 typedef boost::shared_ptr<const FPFHEstimation<PointInT, PointNT, PointOutT> > ConstPtr; 00085 using Feature<PointInT, PointOutT>::feature_name_; 00086 using Feature<PointInT, PointOutT>::getClassName; 00087 using Feature<PointInT, PointOutT>::indices_; 00088 using Feature<PointInT, PointOutT>::k_; 00089 using Feature<PointInT, PointOutT>::search_parameter_; 00090 using Feature<PointInT, PointOutT>::input_; 00091 using Feature<PointInT, PointOutT>::surface_; 00092 using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_; 00093 00094 typedef typename Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut; 00095 00096 /** \brief Empty constructor. */ 00097 FPFHEstimation () : 00098 nr_bins_f1_ (11), nr_bins_f2_ (11), nr_bins_f3_ (11), 00099 hist_f1_ (), hist_f2_ (), hist_f3_ (), fpfh_histogram_ (), 00100 d_pi_ (1.0f / (2.0f * static_cast<float> (M_PI))) 00101 { 00102 feature_name_ = "FPFHEstimation"; 00103 }; 00104 00105 /** \brief Compute the 4-tuple representation containing the three angles and one distance between two points 00106 * represented by Cartesian coordinates and normals. 00107 * \note For explanations about the features, please see the literature mentioned above (the order of the 00108 * features might be different). 00109 * \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points 00110 * \param[in] normals the dataset containing the surface normals (assuming normalized vectors) at each point in cloud 00111 * \param[in] p_idx the index of the first point (source) 00112 * \param[in] q_idx the index of the second point (target) 00113 * \param[out] f1 the first angular feature (angle between the projection of nq_idx and u) 00114 * \param[out] f2 the second angular feature (angle between nq_idx and v) 00115 * \param[out] f3 the third angular feature (angle between np_idx and |p_idx - q_idx|) 00116 * \param[out] f4 the distance feature (p_idx - q_idx) 00117 */ 00118 bool 00119 computePairFeatures (const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals, 00120 int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4); 00121 00122 /** \brief Estimate the SPFH (Simple Point Feature Histograms) individual signatures of the three angular 00123 * (f1, f2, f3) features for a given point based on its spatial neighborhood of 3D points with normals 00124 * \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points 00125 * \param[in] normals the dataset containing the surface normals at each point in \a cloud 00126 * \param[in] p_idx the index of the query point (source) 00127 * \param[in] row the index row in feature histogramms 00128 * \param[in] indices the k-neighborhood point indices in the dataset 00129 * \param[out] hist_f1 the resultant SPFH histogram for feature f1 00130 * \param[out] hist_f2 the resultant SPFH histogram for feature f2 00131 * \param[out] hist_f3 the resultant SPFH histogram for feature f3 00132 */ 00133 void 00134 computePointSPFHSignature (const pcl::PointCloud<PointInT> &cloud, 00135 const pcl::PointCloud<PointNT> &normals, int p_idx, int row, 00136 const std::vector<int> &indices, 00137 Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3); 00138 00139 /** \brief Weight the SPFH (Simple Point Feature Histograms) individual histograms to create the final FPFH 00140 * (Fast Point Feature Histogram) for a given point based on its 3D spatial neighborhood 00141 * \param[in] hist_f1 the histogram feature vector of \a f1 values over the given patch 00142 * \param[in] hist_f2 the histogram feature vector of \a f2 values over the given patch 00143 * \param[in] hist_f3 the histogram feature vector of \a f3 values over the given patch 00144 * \param[in] indices the point indices of p_idx's k-neighborhood in the point cloud 00145 * \param[in] dists the distances from p_idx to all its k-neighbors 00146 * \param[out] fpfh_histogram the resultant FPFH histogram representing the feature at the query point 00147 */ 00148 void 00149 weightPointSPFHSignature (const Eigen::MatrixXf &hist_f1, 00150 const Eigen::MatrixXf &hist_f2, 00151 const Eigen::MatrixXf &hist_f3, 00152 const std::vector<int> &indices, 00153 const std::vector<float> &dists, 00154 Eigen::VectorXf &fpfh_histogram); 00155 00156 /** \brief Set the number of subdivisions for each angular feature interval. 00157 * \param[in] nr_bins_f1 number of subdivisions for the first angular feature 00158 * \param[in] nr_bins_f2 number of subdivisions for the second angular feature 00159 * \param[in] nr_bins_f3 number of subdivisions for the third angular feature 00160 */ 00161 inline void 00162 setNrSubdivisions (int nr_bins_f1, int nr_bins_f2, int nr_bins_f3) 00163 { 00164 nr_bins_f1_ = nr_bins_f1; 00165 nr_bins_f2_ = nr_bins_f2; 00166 nr_bins_f3_ = nr_bins_f3; 00167 } 00168 00169 /** \brief Get the number of subdivisions for each angular feature interval. 00170 * \param[out] nr_bins_f1 number of subdivisions for the first angular feature 00171 * \param[out] nr_bins_f2 number of subdivisions for the second angular feature 00172 * \param[out] nr_bins_f3 number of subdivisions for the third angular feature 00173 */ 00174 inline void 00175 getNrSubdivisions (int &nr_bins_f1, int &nr_bins_f2, int &nr_bins_f3) 00176 { 00177 nr_bins_f1 = nr_bins_f1_; 00178 nr_bins_f2 = nr_bins_f2_; 00179 nr_bins_f3 = nr_bins_f3_; 00180 } 00181 00182 protected: 00183 00184 /** \brief Estimate the set of all SPFH (Simple Point Feature Histograms) signatures for the input cloud 00185 * \param[out] spfh_hist_lookup a lookup table for all the SPF feature indices 00186 * \param[out] hist_f1 the resultant SPFH histogram for feature f1 00187 * \param[out] hist_f2 the resultant SPFH histogram for feature f2 00188 * \param[out] hist_f3 the resultant SPFH histogram for feature f3 00189 */ 00190 void 00191 computeSPFHSignatures (std::vector<int> &spf_hist_lookup, 00192 Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3); 00193 00194 /** \brief Estimate the Fast Point Feature Histograms (FPFH) descriptors at a set of points given by 00195 * <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in 00196 * setSearchMethod () 00197 * \param[out] output the resultant point cloud model dataset that contains the FPFH feature estimates 00198 */ 00199 void 00200 computeFeature (PointCloudOut &output); 00201 00202 /** \brief The number of subdivisions for each angular feature interval. */ 00203 int nr_bins_f1_, nr_bins_f2_, nr_bins_f3_; 00204 00205 /** \brief Placeholder for the f1 histogram. */ 00206 Eigen::MatrixXf hist_f1_; 00207 00208 /** \brief Placeholder for the f2 histogram. */ 00209 Eigen::MatrixXf hist_f2_; 00210 00211 /** \brief Placeholder for the f3 histogram. */ 00212 Eigen::MatrixXf hist_f3_; 00213 00214 /** \brief Placeholder for a point's FPFH signature. */ 00215 Eigen::VectorXf fpfh_histogram_; 00216 00217 /** \brief Float constant = 1.0 / (2.0 * M_PI) */ 00218 float d_pi_; 00219 }; 00220 } 00221 00222 #ifdef PCL_NO_PRECOMPILE 00223 #include <pcl/features/impl/fpfh.hpp> 00224 #endif 00225 00226 #endif //#ifndef PCL_FPFH_H_