407 lines
16 KiB
C++
407 lines
16 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: cvfh.h 4936 2012-03-07 11:12:45Z aaldoma $
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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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namespace pcl
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{
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/** \brief OURCVFHEstimation estimates the Oriented, Unique and Repetable Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given
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* point cloud dataset given XYZ data and normals, as presented in:
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* - OUR-CVFH – Oriented, Unique and Repeatable Clustered Viewpoint Feature Histogram for Object Recognition and 6DOF Pose Estimation
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* A. Aldoma, F. Tombari, R.B. Rusu and M. Vincze
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* DAGM-OAGM 2012
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* Graz, Austria
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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 OURCVFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
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{
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public:
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using Ptr = shared_ptr<OURCVFHEstimation<PointInT, PointNT, PointOutT> >;
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using ConstPtr = shared_ptr<const OURCVFHEstimation<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 PointInTPtr = typename pcl::PointCloud<PointInT>::Ptr;
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/** \brief Empty constructor. */
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OURCVFHEstimation () :
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vpx_ (0), vpy_ (0), vpz_ (0), leaf_size_ (0.005f), normalize_bins_ (false), curv_threshold_ (0.03f), cluster_tolerance_ (leaf_size_ * 3),
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eps_angle_threshold_ (0.125f), min_points_ (50), 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_ = "OURCVFHEstimation";
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refine_clusters_ = 1.f;
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min_axis_value_ = 0.925f;
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axis_ratio_ = 0.8f;
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}
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;
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/** \brief Creates an affine transformation from the RF axes
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* \param[in] evx the x-axis
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* \param[in] evy the y-axis
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* \param[in] evz the z-axis
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* \param[out] transformPC the resulting transformation
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* \param[in] center_mat 4x4 matrix concatenated to the resulting transformation
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*/
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inline Eigen::Matrix4f
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createTransFromAxes (Eigen::Vector3f & evx, Eigen::Vector3f & evy, Eigen::Vector3f & evz, Eigen::Affine3f & transformPC,
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Eigen::Matrix4f & center_mat)
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{
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Eigen::Matrix4f trans;
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trans.setIdentity (4, 4);
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trans (0, 0) = evx (0, 0);
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trans (1, 0) = evx (1, 0);
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trans (2, 0) = evx (2, 0);
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trans (0, 1) = evy (0, 0);
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trans (1, 1) = evy (1, 0);
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trans (2, 1) = evy (2, 0);
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trans (0, 2) = evz (0, 0);
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trans (1, 2) = evz (1, 0);
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trans (2, 2) = evz (2, 0);
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Eigen::Matrix4f homMatrix = Eigen::Matrix4f ();
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homMatrix.setIdentity (4, 4);
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homMatrix = transformPC.matrix ();
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Eigen::Matrix4f trans_copy = trans.inverse ();
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trans = trans_copy * center_mat * homMatrix;
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return trans;
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}
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/** \brief Computes SGURF and the shape distribution based on the selected SGURF
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* \param[in] processed the input cloud
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* \param[out] output the resulting signature
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* \param[in] cluster_indices the indices of the stable cluster
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*/
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void
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computeRFAndShapeDistribution (PointInTPtr & processed, PointCloudOut &output, std::vector<pcl::PointIndices> & cluster_indices);
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/** \brief Computes SGURF
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* \param[in] centroid the centroid of the cluster
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* \param[in] normal_centroid the average of the normals
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* \param[in] processed the input cloud
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* \param[out] transformations the transformations aligning the cloud to the SGURF axes
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* \param[out] grid the cloud transformed internally
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* \param[in] indices the indices of the stable cluster
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*/
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bool
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sgurf (Eigen::Vector3f & centroid, Eigen::Vector3f & normal_centroid, PointInTPtr & processed, std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > & transformations,
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PointInTPtr & grid, pcl::PointIndices & indices);
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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
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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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for (std::size_t i = 0; i < centroids_dominant_orientations_.size (); ++i)
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centroids.push_back (centroids_dominant_orientations_[i]);
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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 (std::size_t i = 0; i < dominant_normals_.size (); ++i)
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centroids.push_back (dominant_normals_[i]);
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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 the 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 Gets the indices of the original point cloud used to compute the signatures
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* \param[out] indices vector of point indices
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*/
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inline void
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getClusterIndices (std::vector<pcl::PointIndices> & indices)
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{
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indices = clusters_;
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}
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/** \brief Gets the number of non-disambiguable axes that correspond to each centroid
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* \param[out] cluster_axes vector mapping each centroid to the number of signatures
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*/
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inline void
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getClusterAxes (std::vector<short> & cluster_axes)
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{
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cluster_axes = cluster_axes_;
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}
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/** \brief Sets the refinement factor for the clusters
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* \param[in] rc the factor used to decide if a point is used to estimate a stable cluster
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*/
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void
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setRefineClusters (float rc)
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{
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refine_clusters_ = rc;
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}
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/** \brief Returns the transformations aligning the point cloud to the corresponding SGURF
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* \param[out] trans vector of transformations
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*/
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void
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getTransforms (std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > & trans)
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{
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trans = transforms_;
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}
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/** \brief Returns a boolean vector indicating of the transformation obtained by getTransforms() represents
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* a valid SGURF
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* \param[out] valid vector of booleans
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*/
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void
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getValidTransformsVec (std::vector<bool> & valid)
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{
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valid = valid_transforms_;
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}
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/** \brief Sets the min axis ratio between the SGURF axes to decide if disambiguition is feasible
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* \param[in] f the ratio between axes
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*/
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void
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setAxisRatio (float f)
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{
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axis_ratio_ = f;
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}
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/** \brief Sets the min disambiguition axis value to generate several SGURFs for the cluster when disambiguition is difficult
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* \param[in] f the min axis value
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*/
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void
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setMinAxisValue (float f)
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{
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min_axis_value_ = f;
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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 Factor for the cluster refinement */
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float refine_clusters_;
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std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > transforms_;
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std::vector<bool> valid_transforms_;
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float axis_ratio_;
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float min_axis_value_;
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/** \brief Estimate the OUR-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 OUR-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, const pcl::PointCloud<pcl::PointNormal> &normals,
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float tolerance, const pcl::search::Search<pcl::PointNormal>::Ptr &tree,
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std::vector<pcl::PointIndices> &clusters, double eps_angle, 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 OUR-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 OUR-CVFH descriptors */
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std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > dominant_normals_;
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/** \brief Indices to the points representing the stable clusters */
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std::vector<pcl::PointIndices> clusters_;
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/** \brief Mapping from clusters to OUR-CVFH descriptors */
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std::vector<short> cluster_axes_;
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/features/impl/our_cvfh.hpp>
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#endif
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