270 lines
11 KiB
C++
270 lines
11 KiB
C++
/*
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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) 2010-2011, Willow Garage, Inc.
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* Copyright (c) 2012-, 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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* $Id$
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*
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*/
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#pragma once
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#include <array>
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#include <pcl/point_types.h>
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#include <pcl/features/feature.h>
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namespace pcl
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{
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/** \brief VFHEstimation estimates the <b>Viewpoint Feature Histogram (VFH)</b> descriptor for a given point cloud
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* dataset containing points and normals. The default VFH implementation uses 45 binning subdivisions for each of
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* the three extended FPFH values, plus another 45 binning subdivisions for the distances between each point and
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* the centroid and 128 binning subdivisions for the viewpoint component, which results in a
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* 308-byte array of float values. These are stored in a pcl::VFHSignature308 point type.
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* A major difference between the PFH/FPFH descriptors and VFH, is that for a given point cloud dataset, only a
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* single VFH descriptor will be estimated (vfhs->size() should be 1), while the resultant PFH/FPFH data
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* will have the same number of entries as the number of points in the cloud.
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*
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* \note If you use this code in any academic work, please cite:
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*
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* - R.B. Rusu, G. Bradski, R. Thibaux, J. Hsu.
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* Fast 3D Recognition and Pose Using the Viewpoint Feature Histogram.
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* In Proceedings of International Conference on Intelligent Robots and Systems (IROS)
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* Taipei, Taiwan, October 18-22 2010.
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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 an example of a parallel implementation of the FPFH (Fast Point Feature Histogram).
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* \author Radu B. Rusu
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* \ingroup features
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*/
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template<typename PointInT, typename PointNT, typename PointOutT = pcl::VFHSignature308>
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class VFHEstimation : 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>::k_;
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using Feature<PointInT, PointOutT>::search_radius_;
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using Feature<PointInT, PointOutT>::input_;
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using Feature<PointInT, PointOutT>::surface_;
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using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
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using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
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using Ptr = shared_ptr<VFHEstimation<PointInT, PointNT, PointOutT> >;
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using ConstPtr = shared_ptr<const VFHEstimation<PointInT, PointNT, PointOutT> >;
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/** \brief Empty constructor. */
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VFHEstimation () :
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nr_bins_f_ ({45, 45, 45, 45}), nr_bins_vp_ (128),
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vpx_ (0), vpy_ (0), vpz_ (0),
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use_given_normal_ (false), use_given_centroid_ (false),
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normalize_bins_ (true), normalize_distances_ (false), size_component_ (false),
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d_pi_ (1.0f / (2.0f * static_cast<float> (M_PI)))
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{
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for (int i = 0; i < 4; ++i)
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{
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hist_f_[i].setZero (nr_bins_f_[i]);
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}
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search_radius_ = 0;
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k_ = 0;
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feature_name_ = "VFHEstimation";
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}
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/** \brief Estimate the SPFH (Simple Point Feature Histograms) signatures of the angular
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* (f1, f2, f3) and distance (f4) features for a given point from its neighborhood
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* \param[in] centroid_p the centroid point
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* \param[in] centroid_n the centroid normal
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* \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points
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* \param[in] normals the dataset containing the surface normals at each point in \a cloud
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* \param[in] indices the k-neighborhood point indices in the dataset
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*/
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void
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computePointSPFHSignature (const Eigen::Vector4f ¢roid_p, const Eigen::Vector4f ¢roid_n,
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const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
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const pcl::Indices &indices);
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/** \brief Set the viewpoint.
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* \param[in] vpx the X coordinate of the viewpoint
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* \param[in] vpy the Y coordinate of the viewpoint
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* \param[in] vpz the Z coordinate of the viewpoint
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*/
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inline void
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setViewPoint (float vpx, float vpy, float vpz)
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{
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vpx_ = vpx;
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vpy_ = vpy;
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vpz_ = vpz;
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}
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/** \brief Get the viewpoint. */
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inline void
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getViewPoint (float &vpx, float &vpy, float &vpz)
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{
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vpx = vpx_;
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vpy = vpy_;
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vpz = vpz_;
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}
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/** \brief Set use_given_normal_
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* \param[in] use Set to true if you want to use the normal passed to setNormalUse(normal)
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*/
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inline void
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setUseGivenNormal (bool use)
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{
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use_given_normal_ = use;
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}
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/** \brief Set the normal to use
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* \param[in] normal Sets the normal to be used in the VFH computation. It is is used
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* to build the Darboux Coordinate system.
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*/
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inline void
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setNormalToUse (const Eigen::Vector3f &normal)
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{
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normal_to_use_ = Eigen::Vector4f (normal[0], normal[1], normal[2], 0);
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}
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/** \brief Set use_given_centroid_
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* \param[in] use Set to true if you want to use the centroid passed through setCentroidToUse(centroid)
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*/
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inline void
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setUseGivenCentroid (bool use)
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{
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use_given_centroid_ = use;
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}
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/** \brief Set centroid_to_use_
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* \param[in] centroid Centroid to be used in the VFH computation. It is used to compute the distances
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* from all points to this centroid.
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*/
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inline void
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setCentroidToUse (const Eigen::Vector3f ¢roid)
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{
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centroid_to_use_ = Eigen::Vector4f (centroid[0], centroid[1], centroid[2], 0);
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}
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/** \brief set normalize_bins_
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* \param[in] normalize If true, the VFH bins are normalized using the total number of points
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*/
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inline void
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setNormalizeBins (bool normalize)
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{
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normalize_bins_ = normalize;
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}
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/** \brief set normalize_distances_
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* \param[in] normalize If true, the 4th component of VFH (shape distribution component) get normalized
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* by the maximum size between the centroid and the point cloud
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*/
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inline void
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setNormalizeDistance (bool normalize)
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{
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normalize_distances_ = normalize;
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}
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/** \brief set size_component_
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* \param[in] fill_size True if the 4th component of VFH (shape distribution component) needs to be filled.
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* Otherwise, it is set to zero.
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*/
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inline void
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setFillSizeComponent (bool fill_size)
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{
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size_component_ = fill_size;
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}
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/** \brief Overloaded computed method from pcl::Feature.
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* \param[out] output the resultant point cloud model dataset containing the estimated features
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*/
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void
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compute (PointCloudOut &output);
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private:
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/** \brief The number of subdivisions for each feature interval. */
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std::array<int, 4> nr_bins_f_;
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int nr_bins_vp_;
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/** \brief Values describing the viewpoint ("pinhole" camera model assumed). For per point viewpoints, inherit
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* from VFHEstimation and provide your own computeFeature (). By default, the viewpoint is set to 0,0,0.
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*/
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float vpx_, vpy_, vpz_;
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/** \brief Estimate the Viewpoint Feature Histograms (VFH) descriptors at 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[out] output the resultant point cloud model dataset that contains the VFH feature estimates
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*/
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void
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computeFeature (PointCloudOut &output) override;
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protected:
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/** \brief This method should get called before starting the actual computation. */
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bool
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initCompute () override;
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/** \brief Placeholder for the f1 histogram. */
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std::array<Eigen::VectorXf, 4> hist_f_;
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/** \brief Placeholder for the vp histogram. */
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Eigen::VectorXf hist_vp_;
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/** \brief Normal to be used to computed VFH. Default, the average normal of the whole point cloud */
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Eigen::Vector4f normal_to_use_;
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/** \brief Centroid to be used to computed VFH. Default, the centroid of the whole point cloud */
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Eigen::Vector4f centroid_to_use_;
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// VFH configuration parameters because CVFH instantiates it. See constructor for default values.
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/** \brief Use the normal_to_use_ */
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bool use_given_normal_;
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/** \brief Use the centroid_to_use_ */
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bool use_given_centroid_;
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/** \brief Normalize bins by the number the total number of points. */
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bool normalize_bins_;
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/** \brief Normalize the shape distribution component of VFH */
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bool normalize_distances_;
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/** \brief Activate or deactivate the size component of VFH */
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bool size_component_;
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private:
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/** \brief Float constant = 1.0 / (2.0 * M_PI) */
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float d_pi_;
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/features/impl/vfh.hpp>
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#endif
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