Point Cloud Library (PCL) 1.14.0
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kdtree.h
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39
40#pragma once
41
42#include <pcl/search/search.h>
43#include <pcl/kdtree/kdtree_flann.h>
44
45namespace pcl
46{
47 // Forward declarations
48 template <typename T> class PointRepresentation;
49
50 namespace search
51 {
52 /** \brief @b search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search
53 * functions using KdTree structure. KdTree is a generic type of 3D spatial locator using kD-tree structures.
54 * The class is making use of the FLANN (Fast Library for Approximate Nearest Neighbor) project
55 * by Marius Muja and David Lowe.
56 *
57 * \author Radu B. Rusu
58 * \ingroup search
59 */
60 template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT> >
61 class KdTree: public Search<PointT>
62 {
63 public:
66
74
75 using Ptr = shared_ptr<KdTree<PointT, Tree> >;
76 using ConstPtr = shared_ptr<const KdTree<PointT, Tree> >;
77
78 using KdTreePtr = typename Tree::Ptr;
79 using KdTreeConstPtr = typename Tree::ConstPtr;
81
82 /** \brief Constructor for KdTree.
83 *
84 * \param[in] sorted set to true if the nearest neighbor search results
85 * need to be sorted in ascending order based on their distance to the
86 * query point
87 *
88 */
89 KdTree (bool sorted = true);
90
91 /** \brief Destructor for KdTree. */
92
93 ~KdTree () override = default;
94
95 /** \brief Provide a pointer to the point representation to use to convert points into k-D vectors.
96 * \param[in] point_representation the const boost shared pointer to a PointRepresentation
97 */
98 void
99 setPointRepresentation (const PointRepresentationConstPtr &point_representation);
100
101 /** \brief Get a pointer to the point representation used when converting points into k-D vectors. */
104 {
105 return (tree_->getPointRepresentation ());
106 }
107
108 /** \brief Sets whether the results have to be sorted or not.
109 * \param[in] sorted_results set to true if the radius search results should be sorted
110 */
111 void
112 setSortedResults (bool sorted_results) override;
113
114 /** \brief Set the search epsilon precision (error bound) for nearest neighbors searches.
115 * \param[in] eps precision (error bound) for nearest neighbors searches
116 */
117 void
118 setEpsilon (float eps);
119
120 /** \brief Get the search epsilon precision (error bound) for nearest neighbors searches. */
121 inline float
122 getEpsilon () const
123 {
124 return (tree_->getEpsilon ());
125 }
126
127 /** \brief Provide a pointer to the input dataset.
128 * \param[in] cloud the const boost shared pointer to a PointCloud message
129 * \param[in] indices the point indices subset that is to be used from \a cloud
130 */
131 bool
132 setInputCloud (const PointCloudConstPtr& cloud,
133 const IndicesConstPtr& indices = IndicesConstPtr ()) override;
134
135 /** \brief Search for the k-nearest neighbors for the given query point.
136 * \param[in] point the given query point
137 * \param[in] k the number of neighbors to search for
138 * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
139 * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
140 * a priori!)
141 * \return number of neighbors found
142 */
143 int
144 nearestKSearch (const PointT &point, int k,
145 Indices &k_indices,
146 std::vector<float> &k_sqr_distances) const override;
147
148 /** \brief Search for all the nearest neighbors of the query point in a given radius.
149 * \param[in] point the given query point
150 * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
151 * \param[out] k_indices the resultant indices of the neighboring points
152 * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
153 * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
154 * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
155 * returned.
156 * \return number of neighbors found in radius
157 */
158 int
159 radiusSearch (const PointT& point, double radius,
160 Indices &k_indices,
161 std::vector<float> &k_sqr_distances,
162 unsigned int max_nn = 0) const override;
163 protected:
164 /** \brief A pointer to the internal KdTree object. */
166 };
167 }
168}
169
170#ifdef PCL_NO_PRECOMPILE
171#include <pcl/search/impl/kdtree.hpp>
172#else
173#define PCL_INSTANTIATE_KdTree(T) template class PCL_EXPORTS pcl::search::KdTree<T>;
174#endif
PointCloud represents the base class in PCL for storing collections of 3D points.
shared_ptr< const PointRepresentation< PointT > > ConstPtr
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition kdtree.h:62
typename Tree::ConstPtr KdTreeConstPtr
Definition kdtree.h:79
int nearestKSearch(const PointT &point, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
Search for the k-nearest neighbors for the given query point.
Definition kdtree.hpp:88
void setEpsilon(float eps)
Set the search epsilon precision (error bound) for nearest neighbors searches.
Definition kdtree.hpp:69
PointRepresentationConstPtr getPointRepresentation() const
Get a pointer to the point representation used when converting points into k-D vectors.
Definition kdtree.h:103
shared_ptr< KdTree< PointT, Tree > > Ptr
Definition kdtree.h:75
float getEpsilon() const
Get the search epsilon precision (error bound) for nearest neighbors searches.
Definition kdtree.h:122
shared_ptr< const KdTree< PointT, Tree > > ConstPtr
Definition kdtree.h:76
void setSortedResults(bool sorted_results) override
Sets whether the results have to be sorted or not.
Definition kdtree.hpp:61
~KdTree() override=default
Destructor for KdTree.
typename Search< PointT >::PointCloudConstPtr PointCloudConstPtr
Definition kdtree.h:65
typename PointRepresentation< PointT >::ConstPtr PointRepresentationConstPtr
Definition kdtree.h:80
void setPointRepresentation(const PointRepresentationConstPtr &point_representation)
Provide a pointer to the point representation to use to convert points into k-D vectors.
Definition kdtree.hpp:53
bool setInputCloud(const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override
Provide a pointer to the input dataset.
Definition kdtree.hpp:76
typename Tree::Ptr KdTreePtr
Definition kdtree.h:78
int radiusSearch(const PointT &point, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const override
Search for all the nearest neighbors of the query point in a given radius.
Definition kdtree.hpp:97
KdTreePtr tree_
A pointer to the internal KdTree object.
Definition kdtree.h:165
typename Search< PointT >::PointCloud PointCloud
Definition kdtree.h:64
Generic search class.
Definition search.h:75
virtual IndicesConstPtr getIndices() const
Get a pointer to the vector of indices used.
Definition search.h:131
PointCloudConstPtr input_
Definition search.h:402
typename PointCloud::ConstPtr PointCloudConstPtr
Definition search.h:79
IndicesConstPtr indices_
Definition search.h:403
virtual PointCloudConstPtr getInputCloud() const
Get a pointer to the input point cloud dataset.
Definition search.h:124
shared_ptr< const Indices > IndicesConstPtr
Definition pcl_base.h:59
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
A point structure representing Euclidean xyz coordinates, and the RGB color.