147 lines
6.4 KiB
C
147 lines
6.4 KiB
C
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/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2009-2014, Willow Garage, Inc.
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* Copyright (c) 2014-, Open Perception, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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*/
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#pragma once
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#include <pcl/features/feature.h>
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#include <pcl/filters/voxel_grid.h>
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namespace pcl
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{
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/** \brief @b GRSDEstimation estimates the Global Radius-based Surface Descriptor (GRSD) for a given point cloud dataset
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* containing points and normals.
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*
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* @note If you use this code in any academic work, please cite (first for the ray-casting and second for the additive version):
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*
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* <ul>
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* <li> Z.C. Marton, D. Pangercic, N. Blodow, Michael Beetz.
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* Combined 2D-3D Categorization and Classification for Multimodal Perception Systems.
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* In The International Journal of Robotics Research, Sage Publications
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* pages 1378--1402, Volume 30, Number 11, September 2011.
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* </li>
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* <li> A. Kanezaki, Z.C. Marton, D. Pangercic, T. Harada, Y. Kuniyoshi, M. Beetz.
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* Voxelized Shape and Color Histograms for RGB-D
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* In the Workshop on Active Semantic Perception and Object Search in the Real World, in conjunction with the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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* San Francisco, California, September 25-30, 2011.
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* </li>
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* </ul>
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*
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* @note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
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* \ref FPFHEstimationOMP for examples on parallel implementations of the FPFH (Fast Point Feature Histogram).
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* \author Zoltan Csaba Marton
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* \ingroup features
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*/
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template <typename PointInT, typename PointNT, typename PointOutT>
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class GRSDEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
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{
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public:
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using Feature<PointInT, PointOutT>::feature_name_;
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using Feature<PointInT, PointOutT>::getClassName;
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using Feature<PointInT, PointOutT>::indices_;
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using Feature<PointInT, PointOutT>::search_radius_;
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using Feature<PointInT, PointOutT>::search_parameter_;
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using Feature<PointInT, PointOutT>::input_;
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using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
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using Feature<PointInT, PointOutT>::setSearchSurface;
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//using Feature<PointInT, PointOutT>::computeFeature;
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using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
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using PointCloudIn = typename Feature<PointInT, PointOutT>::PointCloudIn;
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using PointCloudInPtr = typename Feature<PointInT, PointOutT>::PointCloudInPtr;
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/** \brief Constructor. */
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GRSDEstimation () : additive_ (true)
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{
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feature_name_ = "GRSDEstimation";
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relative_coordinates_all_ = getAllNeighborCellIndices ();
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};
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/** \brief Set the sphere radius that is to be used for determining the nearest neighbors used for the feature
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* estimation. Same value will be used for the internal voxel grid leaf size.
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* \param[in] radius the sphere radius used as the maximum distance to consider a point a neighbor
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*/
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inline void
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setRadiusSearch (double radius) { width_ = search_radius_ = radius; }
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/** \brief Get the sphere radius used for determining the neighbors.
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* \return the sphere radius used as the maximum distance to consider a point a neighbor
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*/
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inline double
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getRadiusSearch () const { return (search_radius_); }
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/** \brief Get the type of the local surface based on the min and max radius computed.
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* \return the integer that represents the type of the local surface with values as
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* Plane (1), Cylinder (2), Noise or corner (0), Sphere (3) and Edge (4)
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*/
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static inline int
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getSimpleType (float min_radius, float max_radius,
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double min_radius_plane = 0.100,
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double max_radius_noise = 0.015,
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double min_radius_cylinder = 0.175,
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double max_min_radius_diff = 0.050);
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protected:
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/** \brief Estimate the Global Radius-based Surface Descriptor (GRSD) for a set of points given by
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* <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in
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* setSearchMethod ()
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* \param output the resultant point cloud that contains the GRSD feature
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*/
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void
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computeFeature (PointCloudOut &output) override;
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private:
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/** \brief Defines if an additive feature is computed or ray-casting is used to get a more descriptive feature. */
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bool additive_;
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/** \brief Defines the voxel size to be used. */
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double width_;
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/** \brief Pre-computed the relative cell indices of all the 26 neighbors. */
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Eigen::MatrixXi relative_coordinates_all_;
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/features/impl/grsd.hpp>
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#endif
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