288 lines
11 KiB
C++
288 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 <pcl/features/feature.h>
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#include <pcl/features/vfh.h>
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#include <pcl/search/search.h> // for Search
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namespace pcl
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{
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/** \brief CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given
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* point cloud dataset containing XYZ data and normals, as presented in:
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* - CAD-Model Recognition and 6 DOF Pose Estimation
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* A. Aldoma, N. Blodow, D. Gossow, S. Gedikli, R.B. Rusu, M. Vincze and G. Bradski
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* ICCV 2011, 3D Representation and Recognition (3dRR11) workshop
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* Barcelona, Spain, (2011)
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*
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* The suggested PointOutT is pcl::VFHSignature308.
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*
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* \author Aitor Aldoma
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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 CVFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
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{
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public:
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using Ptr = shared_ptr<CVFHEstimation<PointInT, PointNT, PointOutT> >;
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using ConstPtr = shared_ptr<const CVFHEstimation<PointInT, PointNT, PointOutT> >;
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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>::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 KdTreePtr = typename pcl::search::Search<PointNormal>::Ptr;
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using VFHEstimator = pcl::VFHEstimation<PointInT, PointNT, pcl::VFHSignature308>;
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/** \brief Empty constructor. */
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CVFHEstimation () :
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vpx_ (0), vpy_ (0), vpz_ (0),
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leaf_size_ (0.005f),
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normalize_bins_ (false),
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curv_threshold_ (0.03f),
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cluster_tolerance_ (leaf_size_ * 3),
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eps_angle_threshold_ (0.125f),
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min_points_ (50),
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radius_normals_ (leaf_size_ * 3)
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{
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search_radius_ = 0;
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k_ = 1;
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feature_name_ = "CVFHEstimation";
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}
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;
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/** \brief Removes normals with high curvature caused by real edges or noisy data
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* \param[in] cloud pointcloud to be filtered
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* \param[in] indices_to_use the indices to use
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* \param[out] indices_out the indices of the points with higher curvature than threshold
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* \param[out] indices_in the indices of the remaining points after filtering
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* \param[in] threshold threshold value for curvature
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*/
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void
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filterNormalsWithHighCurvature (const pcl::PointCloud<PointNT> & cloud, pcl::Indices & indices_to_use, pcl::Indices &indices_out,
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pcl::Indices &indices_in, float threshold);
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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 Set the radius used to compute normals
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* \param[in] radius_normals the radius
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*/
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inline void
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setRadiusNormals (float radius_normals)
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{
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radius_normals_ = radius_normals;
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}
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/** \brief Get the viewpoint.
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* \param[out] vpx the X coordinate of the viewpoint
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* \param[out] vpy the Y coordinate of the viewpoint
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* \param[out] vpz the Z coordinate of the viewpoint
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*/
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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 Get the centroids used to compute different CVFH descriptors
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* \param[out] centroids vector to hold the centroids
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*/
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inline void
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getCentroidClusters (std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > & centroids)
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{
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centroids.insert (centroids.cend (), centroids_dominant_orientations_.cbegin (),
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centroids_dominant_orientations_.cend ());
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}
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/** \brief Get the normal centroids used to compute different CVFH descriptors
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* \param[out] centroids vector to hold the normal centroids
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*/
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inline void
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getCentroidNormalClusters (std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > & centroids)
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{
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for (const auto& normal: dominant_normals_)
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centroids.push_back (normal);
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}
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/** \brief Sets max. Euclidean distance between points to be added to the cluster
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* \param[in] d the maximum Euclidean distance
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*/
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inline void
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setClusterTolerance (float d)
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{
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cluster_tolerance_ = d;
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}
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/** \brief Sets max. deviation of the normals between two points so they can be clustered together
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* \param[in] d the maximum deviation
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*/
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inline void
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setEPSAngleThreshold (float d)
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{
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eps_angle_threshold_ = d;
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}
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/** \brief Sets curvature threshold for removing normals
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* \param[in] d the curvature threshold
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*/
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inline void
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setCurvatureThreshold (float d)
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{
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curv_threshold_ = d;
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}
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/** \brief Set minimum amount of points for a cluster to be considered
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* \param[in] min the minimum amount of points to be set
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*/
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inline void
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setMinPoints (std::size_t min)
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{
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min_points_ = min;
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}
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/** \brief Sets whether if the CVFH signatures should be normalized or not
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* \param[in] normalize true if normalization is required, false otherwise
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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 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 Values describing the viewpoint ("pinhole" camera model assumed).
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* 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 Size of the voxels after voxel gridding. IMPORTANT: Must match the voxel
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* size of the training data or the normalize_bins_ flag must be set to true.
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*/
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float leaf_size_;
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/** \brief Whether to normalize the signatures or not. Default: false. */
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bool normalize_bins_;
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/** \brief Curvature threshold for removing normals. */
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float curv_threshold_;
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/** \brief allowed Euclidean distance between points to be added to the cluster. */
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float cluster_tolerance_;
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/** \brief deviation of the normals between two points so they can be clustered together. */
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float eps_angle_threshold_;
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/** \brief Minimum amount of points in a clustered region to be considered stable for CVFH
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* computation.
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*/
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std::size_t min_points_;
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/** \brief Radius for the normals computation. */
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float radius_normals_;
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/** \brief Estimate the Clustered Viewpoint Feature Histograms (CVFH) descriptors at
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* a set of points given by <setInputCloud (), setIndices ()> using the surface in
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* setSearchSurface ()
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*
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* \param[out] output the resultant point cloud model dataset that contains the CVFH
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* feature estimates
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*/
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void
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computeFeature (PointCloudOut &output) override;
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/** \brief Region growing method using Euclidean distances and neighbors normals to
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* add points to a region.
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* \param[in] cloud point cloud to split into regions
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* \param[in] normals are the normals of cloud
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* \param[in] tolerance is the allowed Euclidean distance between points to be added to
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* the cluster
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* \param[in] tree is the spatial search structure for nearest neighbour search
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* \param[out] clusters vector of indices representing the clustered regions
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* \param[in] eps_angle deviation of the normals between two points so they can be
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* clustered together
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* \param[in] min_pts_per_cluster minimum cluster size. (default: 1 point)
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* \param[in] max_pts_per_cluster maximum cluster size. (default: all the points)
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*/
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void
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extractEuclideanClustersSmooth (const pcl::PointCloud<pcl::PointNormal> &cloud,
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const pcl::PointCloud<pcl::PointNormal> &normals, float tolerance,
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const pcl::search::Search<pcl::PointNormal>::Ptr &tree,
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std::vector<pcl::PointIndices> &clusters, double eps_angle,
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unsigned int min_pts_per_cluster = 1,
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unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ());
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protected:
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/** \brief Centroids that were used to compute different CVFH descriptors */
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std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > centroids_dominant_orientations_;
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/** \brief Normal centroids that were used to compute different CVFH descriptors */
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std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > dominant_normals_;
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/features/impl/cvfh.hpp>
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#endif
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