358 lines
16 KiB
C
358 lines
16 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) 2009-2011, 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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*/
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#pragma once
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#include <pcl/memory.h>
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#include <pcl/pcl_macros.h>
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#include <pcl/point_cloud.h>
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#include <pcl/point_representation.h>
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#include <pcl/common/copy_point.h>
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namespace pcl
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{
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/** \brief KdTree represents the base spatial locator class for kd-tree implementations.
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* \author Radu B Rusu, Bastian Steder, Michael Dixon
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* \ingroup kdtree
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*/
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template <typename PointT>
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class KdTree
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{
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public:
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using IndicesPtr = shared_ptr<Indices >;
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using IndicesConstPtr = shared_ptr<const Indices >;
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using PointCloud = pcl::PointCloud<PointT>;
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using PointCloudPtr = typename PointCloud::Ptr;
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using PointCloudConstPtr = typename PointCloud::ConstPtr;
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using PointRepresentation = pcl::PointRepresentation<PointT>;
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using PointRepresentationConstPtr = typename PointRepresentation::ConstPtr;
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// Boost shared pointers
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using Ptr = shared_ptr<KdTree<PointT> >;
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using ConstPtr = shared_ptr<const KdTree<PointT> >;
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/** \brief Empty constructor for KdTree. Sets some internal values to their defaults.
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* \param[in] sorted set to true if the application that the tree will be used for requires sorted nearest neighbor indices (default). False otherwise.
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*/
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KdTree (bool sorted = true) : input_(),
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epsilon_(0.0f), min_pts_(1), sorted_(sorted),
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point_representation_ (new DefaultPointRepresentation<PointT>)
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{
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};
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/** \brief Provide a pointer to the input dataset.
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* \param[in] cloud the const boost shared pointer to a PointCloud message
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* \param[in] indices the point indices subset that is to be used from \a cloud - if NULL the whole cloud is used
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*/
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virtual void
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setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices = IndicesConstPtr ())
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{
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input_ = cloud;
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indices_ = indices;
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}
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/** \brief Get a pointer to the vector of indices used. */
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inline IndicesConstPtr
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getIndices () const
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{
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return (indices_);
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}
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/** \brief Get a pointer to the input point cloud dataset. */
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inline PointCloudConstPtr
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getInputCloud () const
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{
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return (input_);
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}
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/** \brief Provide a pointer to the point representation to use to convert points into k-D vectors.
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* \param[in] point_representation the const boost shared pointer to a PointRepresentation
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*/
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inline void
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setPointRepresentation (const PointRepresentationConstPtr &point_representation)
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{
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point_representation_ = point_representation;
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if (!input_) return;
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setInputCloud (input_, indices_); // Makes sense in derived classes to reinitialize the tree
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}
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/** \brief Get a pointer to the point representation used when converting points into k-D vectors. */
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inline PointRepresentationConstPtr
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getPointRepresentation () const
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{
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return (point_representation_);
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}
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/** \brief Destructor for KdTree. Deletes all allocated data arrays and destroys the kd-tree structures. */
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virtual ~KdTree () {};
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/** \brief Search for k-nearest neighbors for the given query point.
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* \param[in] p_q the given query point
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* \param[in] k the number of neighbors to search for
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* \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
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* a priori!)
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* \return number of neighbors found
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*/
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virtual int
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nearestKSearch (const PointT &p_q, unsigned int k,
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Indices &k_indices, std::vector<float> &k_sqr_distances) const = 0;
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/** \brief Search for k-nearest neighbors for the given query point.
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*
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* \attention This method does not do any bounds checking for the input index
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* (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
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*
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* \param[in] cloud the point cloud data
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* \param[in] index a \a valid index in \a cloud representing a \a valid (i.e., finite) query point
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* \param[in] k the number of neighbors to search for
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* \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
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* a priori!)
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*
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* \return number of neighbors found
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*
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* \exception asserts in debug mode if the index is not between 0 and the maximum number of points
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*/
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virtual int
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nearestKSearch (const PointCloud &cloud, int index, unsigned int k,
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Indices &k_indices, std::vector<float> &k_sqr_distances) const
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{
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assert (index >= 0 && index < static_cast<int> (cloud.size ()) && "Out-of-bounds error in nearestKSearch!");
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return (nearestKSearch (cloud[index], k, k_indices, k_sqr_distances));
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}
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/** \brief Search for k-nearest neighbors for the given query point.
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* This method accepts a different template parameter for the point type.
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* \param[in] point the given query point
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* \param[in] k the number of neighbors to search for
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* \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
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* a priori!)
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* \return number of neighbors found
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*/
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template <typename PointTDiff> inline int
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nearestKSearchT (const PointTDiff &point, unsigned int k,
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Indices &k_indices, std::vector<float> &k_sqr_distances) const
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{
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PointT p;
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copyPoint (point, p);
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return (nearestKSearch (p, k, k_indices, k_sqr_distances));
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}
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/** \brief Search for k-nearest neighbors for the given query point (zero-copy).
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*
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* \attention This method does not do any bounds checking for the input index
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* (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
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*
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* \param[in] index a \a valid index representing a \a valid query point in the dataset given
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* by \a setInputCloud. If indices were given in setInputCloud, index will be the position in
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* the indices vector.
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*
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* \param[in] k the number of neighbors to search for
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* \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
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* a priori!)
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* \return number of neighbors found
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*
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* \exception asserts in debug mode if the index is not between 0 and the maximum number of points
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*/
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virtual int
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nearestKSearch (int index, unsigned int k,
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Indices &k_indices, std::vector<float> &k_sqr_distances) const
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{
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if (indices_ == nullptr)
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{
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assert (index >= 0 && index < static_cast<int> (input_->size ()) && "Out-of-bounds error in nearestKSearch!");
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return (nearestKSearch ((*input_)[index], k, k_indices, k_sqr_distances));
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}
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assert (index >= 0 && index < static_cast<int> (indices_->size ()) && "Out-of-bounds error in nearestKSearch!");
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return (nearestKSearch ((*input_)[(*indices_)[index]], k, k_indices, k_sqr_distances));
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}
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/** \brief Search for all the nearest neighbors of the query point in a given radius.
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* \param[in] p_q the given query point
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* \param[in] radius the radius of the sphere bounding all of p_q's neighbors
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* \param[out] k_indices the resultant indices of the neighboring points
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points
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* \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
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* 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
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* returned.
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* \return number of neighbors found in radius
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*/
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virtual int
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radiusSearch (const PointT &p_q, double radius, Indices &k_indices,
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std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const = 0;
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/** \brief Search for all the nearest neighbors of the query point in a given radius.
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*
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* \attention This method does not do any bounds checking for the input index
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* (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
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*
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* \param[in] cloud the point cloud data
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* \param[in] index a \a valid index in \a cloud representing a \a valid (i.e., finite) query point
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* \param[in] radius the radius of the sphere bounding all of p_q's neighbors
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* \param[out] k_indices the resultant indices of the neighboring points
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points
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* \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
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* 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
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* returned.
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* \return number of neighbors found in radius
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*
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* \exception asserts in debug mode if the index is not between 0 and the maximum number of points
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*/
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virtual int
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radiusSearch (const PointCloud &cloud, int index, double radius,
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Indices &k_indices, std::vector<float> &k_sqr_distances,
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unsigned int max_nn = 0) const
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{
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assert (index >= 0 && index < static_cast<int> (cloud.size ()) && "Out-of-bounds error in radiusSearch!");
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return (radiusSearch(cloud[index], radius, k_indices, k_sqr_distances, max_nn));
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}
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/** \brief Search for all the nearest neighbors of the query point in a given radius.
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* \param[in] point the given query point
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* \param[in] radius the radius of the sphere bounding all of p_q's neighbors
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* \param[out] k_indices the resultant indices of the neighboring points
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points
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* \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
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* 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
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* returned.
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* \return number of neighbors found in radius
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*/
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template <typename PointTDiff> inline int
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radiusSearchT (const PointTDiff &point, double radius, Indices &k_indices,
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std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
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{
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PointT p;
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copyPoint (point, p);
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return (radiusSearch (p, radius, k_indices, k_sqr_distances, max_nn));
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}
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/** \brief Search for all the nearest neighbors of the query point in a given radius (zero-copy).
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*
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* \attention This method does not do any bounds checking for the input index
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* (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
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*
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* \param[in] index a \a valid index representing a \a valid query point in the dataset given
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* by \a setInputCloud. If indices were given in setInputCloud, index will be the position in
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* the indices vector.
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*
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* \param[in] radius the radius of the sphere bounding all of p_q's neighbors
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* \param[out] k_indices the resultant indices of the neighboring points
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points
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* \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
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* 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
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* returned.
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* \return number of neighbors found in radius
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*
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* \exception asserts in debug mode if the index is not between 0 and the maximum number of points
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*/
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virtual int
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radiusSearch (int index, double radius, Indices &k_indices,
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std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
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{
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if (indices_ == nullptr)
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{
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assert (index >= 0 && index < static_cast<int> (input_->size ()) && "Out-of-bounds error in radiusSearch!");
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return (radiusSearch ((*input_)[index], radius, k_indices, k_sqr_distances, max_nn));
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}
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assert (index >= 0 && index < static_cast<int> (indices_->size ()) && "Out-of-bounds error in radiusSearch!");
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return (radiusSearch ((*input_)[(*indices_)[index]], radius, k_indices, k_sqr_distances, max_nn));
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}
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/** \brief Set the search epsilon precision (error bound) for nearest neighbors searches.
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* \param[in] eps precision (error bound) for nearest neighbors searches
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*/
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virtual inline void
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setEpsilon (float eps)
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{
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epsilon_ = eps;
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}
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/** \brief Get the search epsilon precision (error bound) for nearest neighbors searches. */
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inline float
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getEpsilon () const
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{
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return (epsilon_);
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}
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/** \brief Minimum allowed number of k nearest neighbors points that a viable result must contain.
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* \param[in] min_pts the minimum number of neighbors in a viable neighborhood
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*/
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inline void
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setMinPts (int min_pts)
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{
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min_pts_ = min_pts;
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}
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/** \brief Get the minimum allowed number of k nearest neighbors points that a viable result must contain. */
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inline int
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getMinPts () const
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{
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return (min_pts_);
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}
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protected:
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/** \brief The input point cloud dataset containing the points we need to use. */
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PointCloudConstPtr input_;
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/** \brief A pointer to the vector of point indices to use. */
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IndicesConstPtr indices_;
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/** \brief Epsilon precision (error bound) for nearest neighbors searches. */
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float epsilon_;
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||
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/** \brief Minimum allowed number of k nearest neighbors points that a viable result must contain. */
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int min_pts_;
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/** \brief Return the radius search neighbours sorted **/
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bool sorted_;
|
||
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/** \brief For converting different point structures into k-dimensional vectors for nearest-neighbor search. */
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PointRepresentationConstPtr point_representation_;
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||
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/** \brief Class getName method. */
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virtual std::string
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getName () const = 0;
|
||
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};
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||
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}
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