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 * 00007 * All rights reserved. 00008 * 00009 * Redistribution and use in source and binary forms, with or without 00010 * modification, are permitted provided that the following conditions 00011 * are met: 00012 * 00013 * * Redistributions of source code must retain the above copyright 00014 * notice, this list of conditions and the following disclaimer. 00015 * * Redistributions in binary form must reproduce the above 00016 * copyright notice, this list of conditions and the following 00017 * disclaimer in the documentation and/or other materials provided 00018 * with the distribution. 00019 * * Neither the name of Willow Garage, Inc. nor the names of its 00020 * contributors may be used to endorse or promote products derived 00021 * from this software without specific prior written permission. 00022 * 00023 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 00024 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 00025 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 00026 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 00027 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00028 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00029 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00030 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00031 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00032 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00033 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00034 * POSSIBILITY OF SUCH DAMAGE. 00035 * 00036 * $Id$ 00037 * 00038 */ 00039 00040 #ifndef PCL_KEYPOINTS_UNIFORM_SAMPLING_H_ 00041 #define PCL_KEYPOINTS_UNIFORM_SAMPLING_H_ 00042 00043 #include <pcl/keypoints/keypoint.h> 00044 #include <boost/unordered_map.hpp> 00045 00046 namespace pcl 00047 { 00048 /** \brief @b UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data. 00049 * 00050 * The @b UniformSampling class creates a *3D voxel grid* (think about a voxel 00051 * grid as a set of tiny 3D boxes in space) over the input point cloud data. 00052 * Then, in each *voxel* (i.e., 3D box), all the points present will be 00053 * approximated (i.e., *downsampled*) with their centroid. This approach is 00054 * a bit slower than approximating them with the center of the voxel, but it 00055 * represents the underlying surface more accurately. 00056 * 00057 * \author Radu Bogdan Rusu 00058 * \ingroup keypoints 00059 */ 00060 template <typename PointInT> 00061 class UniformSampling: public Keypoint<PointInT, int> 00062 { 00063 typedef typename Keypoint<PointInT, int>::PointCloudIn PointCloudIn; 00064 typedef typename Keypoint<PointInT, int>::PointCloudOut PointCloudOut; 00065 00066 using Keypoint<PointInT, int>::name_; 00067 using Keypoint<PointInT, int>::input_; 00068 using Keypoint<PointInT, int>::indices_; 00069 using Keypoint<PointInT, int>::search_radius_; 00070 using Keypoint<PointInT, int>::getClassName; 00071 00072 public: 00073 typedef boost::shared_ptr<UniformSampling<PointInT> > Ptr; 00074 typedef boost::shared_ptr<const UniformSampling<PointInT> > ConstPtr; 00075 00076 /** \brief Empty constructor. */ 00077 UniformSampling () : 00078 leaves_ (), 00079 leaf_size_ (Eigen::Vector4f::Zero ()), 00080 inverse_leaf_size_ (Eigen::Vector4f::Zero ()), 00081 min_b_ (Eigen::Vector4i::Zero ()), 00082 max_b_ (Eigen::Vector4i::Zero ()), 00083 div_b_ (Eigen::Vector4i::Zero ()), 00084 divb_mul_ (Eigen::Vector4i::Zero ()) 00085 { 00086 name_ = "UniformSampling"; 00087 } 00088 00089 /** \brief Destructor. */ 00090 virtual ~UniformSampling () 00091 { 00092 leaves_.clear(); 00093 } 00094 00095 /** \brief Set the 3D grid leaf size. 00096 * \param radius the 3D grid leaf size 00097 */ 00098 virtual inline void 00099 setRadiusSearch (double radius) 00100 { 00101 leaf_size_[0] = leaf_size_[1] = leaf_size_[2] = static_cast<float> (radius); 00102 // Avoid division errors 00103 if (leaf_size_[3] == 0) 00104 leaf_size_[3] = 1; 00105 // Use multiplications instead of divisions 00106 inverse_leaf_size_ = Eigen::Array4f::Ones () / leaf_size_.array (); 00107 search_radius_ = radius; 00108 } 00109 00110 protected: 00111 /** \brief Simple structure to hold an nD centroid and the number of points in a leaf. */ 00112 struct Leaf 00113 { 00114 Leaf () : idx (-1) { } 00115 int idx; 00116 }; 00117 00118 /** \brief The 3D grid leaves. */ 00119 boost::unordered_map<size_t, Leaf> leaves_; 00120 00121 /** \brief The size of a leaf. */ 00122 Eigen::Vector4f leaf_size_; 00123 00124 /** \brief Internal leaf sizes stored as 1/leaf_size_ for efficiency reasons. */ 00125 Eigen::Array4f inverse_leaf_size_; 00126 00127 /** \brief The minimum and maximum bin coordinates, the number of divisions, and the division multiplier. */ 00128 Eigen::Vector4i min_b_, max_b_, div_b_, divb_mul_; 00129 00130 /** \brief Downsample a Point Cloud using a voxelized grid approach 00131 * \param output the resultant point cloud message 00132 */ 00133 void 00134 detectKeypoints (PointCloudOut &output); 00135 }; 00136 } 00137 00138 #ifdef PCL_NO_PRECOMPILE 00139 #include <pcl/keypoints/impl/uniform_sampling.hpp> 00140 #endif 00141 00142 #endif //#ifndef PCL_KEYPOINTS_UNIFORM_SAMPLING_H_ 00143