Point Cloud Library (PCL) 1.12.1
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
94 {
95 }
96
97 /** \brief Provide a pointer to the point representation to use to convert points into k-D vectors.
98 * \param[in] point_representation the const boost shared pointer to a PointRepresentation
99 */
100 void
101 setPointRepresentation (const PointRepresentationConstPtr &point_representation);
102
103 /** \brief Get a pointer to the point representation used when converting points into k-D vectors. */
106 {
107 return (tree_->getPointRepresentation ());
108 }
109
110 /** \brief Sets whether the results have to be sorted or not.
111 * \param[in] sorted_results set to true if the radius search results should be sorted
112 */
113 void
114 setSortedResults (bool sorted_results) override;
115
116 /** \brief Set the search epsilon precision (error bound) for nearest neighbors searches.
117 * \param[in] eps precision (error bound) for nearest neighbors searches
118 */
119 void
120 setEpsilon (float eps);
121
122 /** \brief Get the search epsilon precision (error bound) for nearest neighbors searches. */
123 inline float
124 getEpsilon () const
125 {
126 return (tree_->getEpsilon ());
127 }
128
129 /** \brief Provide a pointer to the input dataset.
130 * \param[in] cloud the const boost shared pointer to a PointCloud message
131 * \param[in] indices the point indices subset that is to be used from \a cloud
132 */
133 void
134 setInputCloud (const PointCloudConstPtr& cloud,
135 const IndicesConstPtr& indices = IndicesConstPtr ()) override;
136
137 /** \brief Search for the k-nearest neighbors for the given query point.
138 * \param[in] point the given query point
139 * \param[in] k the number of neighbors to search for
140 * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
141 * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
142 * a priori!)
143 * \return number of neighbors found
144 */
145 int
146 nearestKSearch (const PointT &point, int k,
147 Indices &k_indices,
148 std::vector<float> &k_sqr_distances) const override;
149
150 /** \brief Search for all the nearest neighbors of the query point in a given radius.
151 * \param[in] point the given query point
152 * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
153 * \param[out] k_indices the resultant indices of the neighboring points
154 * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
155 * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
156 * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
157 * returned.
158 * \return number of neighbors found in radius
159 */
160 int
161 radiusSearch (const PointT& point, double radius,
162 Indices &k_indices,
163 std::vector<float> &k_sqr_distances,
164 unsigned int max_nn = 0) const override;
165 protected:
166 /** \brief A pointer to the internal KdTree object. */
168 };
169 }
170}
171
172#ifdef PCL_NO_PRECOMPILE
173#include <pcl/search/impl/kdtree.hpp>
174#else
175#define PCL_INSTANTIATE_KdTree(T) template class PCL_EXPORTS pcl::search::KdTree<T>;
176#endif
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
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:87
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:105
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:124
shared_ptr< const KdTree< PointT, Tree > > ConstPtr
Definition: kdtree.h:76
void setInputCloud(const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override
Provide a pointer to the input dataset.
Definition: kdtree.hpp:76
void setSortedResults(bool sorted_results) override
Sets whether the results have to be sorted or not.
Definition: kdtree.hpp:61
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
KdTree(bool sorted=true)
Constructor for KdTree.
Definition: kdtree.hpp:45
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:96
KdTreePtr tree_
A pointer to the internal KdTree object.
Definition: kdtree.h:167
~KdTree()
Destructor for KdTree.
Definition: kdtree.h:93
typename Search< PointT >::PointCloud PointCloud
Definition: kdtree.h:64
Generic search class.
Definition: search.h:75
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: search.h:79
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.