Point Cloud Library (PCL)  1.14.0-dev
kdtree.h
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38 
39 #pragma once
40 
41 #include <pcl/memory.h>
42 #include <pcl/pcl_macros.h>
43 #include <pcl/point_cloud.h>
44 #include <pcl/point_representation.h>
45 #include <pcl/common/copy_point.h>
46 #include <cassert>
47 
48 namespace pcl
49 {
50  /** \brief KdTree represents the base spatial locator class for kd-tree implementations.
51  * \author Radu B Rusu, Bastian Steder, Michael Dixon
52  * \ingroup kdtree
53  */
54  template <typename PointT>
55  class KdTree
56  {
57  public:
58  using IndicesPtr = shared_ptr<Indices >;
59  using IndicesConstPtr = shared_ptr<const Indices >;
60 
62  using PointCloudPtr = typename PointCloud::Ptr;
64 
67 
68  // Boost shared pointers
69  using Ptr = shared_ptr<KdTree<PointT> >;
70  using ConstPtr = shared_ptr<const KdTree<PointT> >;
71 
72  /** \brief Empty constructor for KdTree. Sets some internal values to their defaults.
73  * \param[in] sorted set to true if the application that the tree will be used for requires sorted nearest neighbor indices (default). False otherwise.
74  */
75  KdTree (bool sorted = true) : input_(),
76  sorted_(sorted),
78  {
79  };
80 
81  /** \brief Provide a pointer to the input dataset.
82  * \param[in] cloud the const boost shared pointer to a PointCloud message
83  * \param[in] indices the point indices subset that is to be used from \a cloud - if NULL the whole cloud is used
84  */
85  virtual void
87  {
88  input_ = cloud;
89  indices_ = indices;
90  }
91 
92  /** \brief Get a pointer to the vector of indices used. */
93  inline IndicesConstPtr
94  getIndices () const
95  {
96  return (indices_);
97  }
98 
99  /** \brief Get a pointer to the input point cloud dataset. */
100  inline PointCloudConstPtr
101  getInputCloud () const
102  {
103  return (input_);
104  }
105 
106  /** \brief Provide a pointer to the point representation to use to convert points into k-D vectors.
107  * \param[in] point_representation the const boost shared pointer to a PointRepresentation
108  */
109  inline void
111  {
112  point_representation_ = point_representation;
113  if (!input_) return;
114  setInputCloud (input_, indices_); // Makes sense in derived classes to reinitialize the tree
115  }
116 
117  /** \brief Get a pointer to the point representation used when converting points into k-D vectors. */
120  {
121  return (point_representation_);
122  }
123 
124  /** \brief Destructor for KdTree. Deletes all allocated data arrays and destroys the kd-tree structures. */
125  virtual ~KdTree () = default;
126 
127  /** \brief Search for k-nearest neighbors for the given query point.
128  * \param[in] p_q the given query point
129  * \param[in] k the number of neighbors to search for
130  * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
131  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
132  * a priori!)
133  * \return number of neighbors found
134  */
135  virtual int
136  nearestKSearch (const PointT &p_q, unsigned int k,
137  Indices &k_indices, std::vector<float> &k_sqr_distances) const = 0;
138 
139  /** \brief Search for k-nearest neighbors for the given query point.
140  *
141  * \attention This method does not do any bounds checking for the input index
142  * (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
143  *
144  * \param[in] cloud the point cloud data
145  * \param[in] index a \a valid index in \a cloud representing a \a valid (i.e., finite) query point
146  * \param[in] k the number of neighbors to search for
147  * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
148  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
149  * a priori!)
150  *
151  * \return number of neighbors found
152  *
153  * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
154  */
155  virtual int
156  nearestKSearch (const PointCloud &cloud, int index, unsigned int k,
157  Indices &k_indices, std::vector<float> &k_sqr_distances) const
158  {
159  assert (index >= 0 && index < static_cast<int> (cloud.size ()) && "Out-of-bounds error in nearestKSearch!");
160  return (nearestKSearch (cloud[index], k, k_indices, k_sqr_distances));
161  }
162 
163  /** \brief Search for k-nearest neighbors for the given query point.
164  * This method accepts a different template parameter for the point type.
165  * \param[in] point the given query point
166  * \param[in] k the number of neighbors to search for
167  * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
168  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
169  * a priori!)
170  * \return number of neighbors found
171  */
172  template <typename PointTDiff> inline int
173  nearestKSearchT (const PointTDiff &point, unsigned int k,
174  Indices &k_indices, std::vector<float> &k_sqr_distances) const
175  {
176  PointT p;
177  copyPoint (point, p);
178  return (nearestKSearch (p, k, k_indices, k_sqr_distances));
179  }
180 
181  /** \brief Search for k-nearest neighbors for the given query point (zero-copy).
182  *
183  * \attention This method does not do any bounds checking for the input index
184  * (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
185  *
186  * \param[in] index a \a valid index representing a \a valid query point in the dataset given
187  * by \a setInputCloud. If indices were given in setInputCloud, index will be the position in
188  * the indices vector.
189  *
190  * \param[in] k the number of neighbors to search for
191  * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
192  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
193  * a priori!)
194  * \return number of neighbors found
195  *
196  * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
197  */
198  virtual int
199  nearestKSearch (int index, unsigned int k,
200  Indices &k_indices, std::vector<float> &k_sqr_distances) const
201  {
202  if (indices_ == nullptr)
203  {
204  assert (index >= 0 && index < static_cast<int> (input_->size ()) && "Out-of-bounds error in nearestKSearch!");
205  return (nearestKSearch ((*input_)[index], k, k_indices, k_sqr_distances));
206  }
207  assert (index >= 0 && index < static_cast<int> (indices_->size ()) && "Out-of-bounds error in nearestKSearch!");
208 
209  return (nearestKSearch ((*input_)[(*indices_)[index]], k, k_indices, k_sqr_distances));
210  }
211 
212  /** \brief Search for all the nearest neighbors of the query point in a given radius.
213  * \param[in] p_q the given query point
214  * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
215  * \param[out] k_indices the resultant indices of the neighboring points
216  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
217  * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
218  * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
219  * returned.
220  * \return number of neighbors found in radius
221  */
222  virtual int
223  radiusSearch (const PointT &p_q, double radius, Indices &k_indices,
224  std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const = 0;
225 
226  /** \brief Search for all the nearest neighbors of the query point in a given radius.
227  *
228  * \attention This method does not do any bounds checking for the input index
229  * (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
230  *
231  * \param[in] cloud the point cloud data
232  * \param[in] index a \a valid index in \a cloud representing a \a valid (i.e., finite) query point
233  * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
234  * \param[out] k_indices the resultant indices of the neighboring points
235  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
236  * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
237  * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
238  * returned.
239  * \return number of neighbors found in radius
240  *
241  * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
242  */
243  virtual int
244  radiusSearch (const PointCloud &cloud, int index, double radius,
245  Indices &k_indices, std::vector<float> &k_sqr_distances,
246  unsigned int max_nn = 0) const
247  {
248  assert (index >= 0 && index < static_cast<int> (cloud.size ()) && "Out-of-bounds error in radiusSearch!");
249  return (radiusSearch(cloud[index], radius, k_indices, k_sqr_distances, max_nn));
250  }
251 
252  /** \brief Search for all the nearest neighbors of the query point in a given radius.
253  * \param[in] point the given query point
254  * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
255  * \param[out] k_indices the resultant indices of the neighboring points
256  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
257  * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
258  * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
259  * returned.
260  * \return number of neighbors found in radius
261  */
262  template <typename PointTDiff> inline int
263  radiusSearchT (const PointTDiff &point, double radius, Indices &k_indices,
264  std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
265  {
266  PointT p;
267  copyPoint (point, p);
268  return (radiusSearch (p, radius, k_indices, k_sqr_distances, max_nn));
269  }
270 
271  /** \brief Search for all the nearest neighbors of the query point in a given radius (zero-copy).
272  *
273  * \attention This method does not do any bounds checking for the input index
274  * (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
275  *
276  * \param[in] index a \a valid index representing a \a valid query point in the dataset given
277  * by \a setInputCloud. If indices were given in setInputCloud, index will be the position in
278  * the indices vector.
279  *
280  * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
281  * \param[out] k_indices the resultant indices of the neighboring points
282  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
283  * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
284  * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
285  * returned.
286  * \return number of neighbors found in radius
287  *
288  * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
289  */
290  virtual int
291  radiusSearch (int index, double radius, Indices &k_indices,
292  std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
293  {
294  if (indices_ == nullptr)
295  {
296  assert (index >= 0 && index < static_cast<int> (input_->size ()) && "Out-of-bounds error in radiusSearch!");
297  return (radiusSearch ((*input_)[index], radius, k_indices, k_sqr_distances, max_nn));
298  }
299  assert (index >= 0 && index < static_cast<int> (indices_->size ()) && "Out-of-bounds error in radiusSearch!");
300  return (radiusSearch ((*input_)[(*indices_)[index]], radius, k_indices, k_sqr_distances, max_nn));
301  }
302 
303  /** \brief Set the search epsilon precision (error bound) for nearest neighbors searches.
304  * \param[in] eps precision (error bound) for nearest neighbors searches
305  */
306  virtual inline void
307  setEpsilon (float eps)
308  {
309  epsilon_ = eps;
310  }
311 
312  /** \brief Get the search epsilon precision (error bound) for nearest neighbors searches. */
313  inline float
314  getEpsilon () const
315  {
316  return (epsilon_);
317  }
318 
319  /** \brief Minimum allowed number of k nearest neighbors points that a viable result must contain.
320  * \param[in] min_pts the minimum number of neighbors in a viable neighborhood
321  */
322  inline void
323  setMinPts (int min_pts)
324  {
325  min_pts_ = min_pts;
326  }
327 
328  /** \brief Get the minimum allowed number of k nearest neighbors points that a viable result must contain. */
329  inline int
330  getMinPts () const
331  {
332  return (min_pts_);
333  }
334 
335  protected:
336  /** \brief The input point cloud dataset containing the points we need to use. */
338 
339  /** \brief A pointer to the vector of point indices to use. */
341 
342  /** \brief Epsilon precision (error bound) for nearest neighbors searches. */
343  float epsilon_{0.0f};
344 
345  /** \brief Minimum allowed number of k nearest neighbors points that a viable result must contain. */
346  int min_pts_{1};
347 
348  /** \brief Return the radius search neighbours sorted **/
349  bool sorted_;
350 
351  /** \brief For converting different point structures into k-dimensional vectors for nearest-neighbor search. */
353 
354  /** \brief Class getName method. */
355  virtual std::string
356  getName () const = 0;
357  };
358 }
DefaultPointRepresentation extends PointRepresentation to define default behavior for common point ty...
KdTree represents the base spatial locator class for kd-tree implementations.
Definition: kdtree.h:56
virtual int nearestKSearch(int index, unsigned int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
Search for k-nearest neighbors for the given query point (zero-copy).
Definition: kdtree.h:199
shared_ptr< const Indices > IndicesConstPtr
Definition: kdtree.h:59
PointRepresentationConstPtr point_representation_
For converting different point structures into k-dimensional vectors for nearest-neighbor search.
Definition: kdtree.h:352
PointCloudConstPtr getInputCloud() const
Get a pointer to the input point cloud dataset.
Definition: kdtree.h:101
KdTree(bool sorted=true)
Empty constructor for KdTree.
Definition: kdtree.h:75
typename PointRepresentation::ConstPtr PointRepresentationConstPtr
Definition: kdtree.h:66
virtual int radiusSearch(int index, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const
Search for all the nearest neighbors of the query point in a given radius (zero-copy).
Definition: kdtree.h:291
IndicesConstPtr getIndices() const
Get a pointer to the vector of indices used.
Definition: kdtree.h:94
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: kdtree.h:63
virtual ~KdTree()=default
Destructor for KdTree.
typename PointCloud::Ptr PointCloudPtr
Definition: kdtree.h:62
virtual int radiusSearch(const PointCloud &cloud, int index, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const
Search for all the nearest neighbors of the query point in a given radius.
Definition: kdtree.h:244
PointCloudConstPtr input_
The input point cloud dataset containing the points we need to use.
Definition: kdtree.h:337
float getEpsilon() const
Get the search epsilon precision (error bound) for nearest neighbors searches.
Definition: kdtree.h:314
virtual void setEpsilon(float eps)
Set the search epsilon precision (error bound) for nearest neighbors searches.
Definition: kdtree.h:307
float epsilon_
Epsilon precision (error bound) for nearest neighbors searches.
Definition: kdtree.h:343
int min_pts_
Minimum allowed number of k nearest neighbors points that a viable result must contain.
Definition: kdtree.h:346
shared_ptr< Indices > IndicesPtr
Definition: kdtree.h:58
int nearestKSearchT(const PointTDiff &point, unsigned int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
Search for k-nearest neighbors for the given query point.
Definition: kdtree.h:173
void setPointRepresentation(const PointRepresentationConstPtr &point_representation)
Provide a pointer to the point representation to use to convert points into k-D vectors.
Definition: kdtree.h:110
void setMinPts(int min_pts)
Minimum allowed number of k nearest neighbors points that a viable result must contain.
Definition: kdtree.h:323
virtual void setInputCloud(const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr())
Provide a pointer to the input dataset.
Definition: kdtree.h:86
bool sorted_
Return the radius search neighbours sorted.
Definition: kdtree.h:349
int getMinPts() const
Get the minimum allowed number of k nearest neighbors points that a viable result must contain.
Definition: kdtree.h:330
virtual int nearestKSearch(const PointCloud &cloud, int index, unsigned int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
Search for k-nearest neighbors for the given query point.
Definition: kdtree.h:156
virtual int radiusSearch(const PointT &p_q, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const =0
Search for all the nearest neighbors of the query point in a given radius.
IndicesConstPtr indices_
A pointer to the vector of point indices to use.
Definition: kdtree.h:340
shared_ptr< KdTree< PointT > > Ptr
Definition: kdtree.h:69
int radiusSearchT(const PointTDiff &point, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const
Search for all the nearest neighbors of the query point in a given radius.
Definition: kdtree.h:263
virtual int nearestKSearch(const PointT &p_q, unsigned int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const =0
Search for k-nearest neighbors for the given query point.
shared_ptr< const KdTree< PointT > > ConstPtr
Definition: kdtree.h:70
virtual std::string getName() const =0
Class getName method.
PointRepresentationConstPtr getPointRepresentation() const
Get a pointer to the point representation used when converting points into k-D vectors.
Definition: kdtree.h:119
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
std::size_t size() const
Definition: point_cloud.h:443
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:413
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:414
PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensi...
shared_ptr< const PointRepresentation< PointT > > ConstPtr
void copyPoint(const PointInT &point_in, PointOutT &point_out)
Copy the fields of a source point into a target point.
Definition: copy_point.hpp:137
Defines functions, macros and traits for allocating and using memory.
shared_ptr< const Indices > IndicesConstPtr
Definition: pcl_base.h:59
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
Defines all the PCL and non-PCL macros used.
A point structure representing Euclidean xyz coordinates, and the RGB color.