Point Cloud Library (PCL)  1.12.1-dev
octree.h
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38 
39 #pragma once
40 
41 #include <pcl/search/search.h>
42 #include <pcl/octree/octree_search.h>
43 
44 namespace pcl
45 {
46  namespace search
47  {
48  /** \brief @b search::Octree is a wrapper class which implements nearest neighbor search operations based on the
49  * pcl::octree::Octree structure.
50  *
51  * The octree pointcloud class needs to be initialized with its voxel
52  * resolution. Its bounding box is automatically adjusted according to the
53  * pointcloud dimension or it can be predefined. Note: The tree depth
54  * equates to the resolution and the bounding box dimensions of the
55  * octree.
56  *
57  * \note typename: PointT: type of point used in pointcloud
58  * \note typename: LeafT: leaf node class (usuallt templated with integer indices values)
59  * \note typename: OctreeT: octree implementation ()
60  *
61  * \author Julius Kammerl
62  * \ingroup search
63  */
64  template<typename PointT,
65  typename LeafTWrap = pcl::octree::OctreeContainerPointIndices,
66  typename BranchTWrap = pcl::octree::OctreeContainerEmpty,
68  class Octree: public Search<PointT>
69  {
70  public:
71  // public typedefs
72  using Ptr = shared_ptr<pcl::search::Octree<PointT,LeafTWrap,BranchTWrap,OctreeT> >;
73  using ConstPtr = shared_ptr<const pcl::search::Octree<PointT,LeafTWrap,BranchTWrap,OctreeT> >;
74 
76  using PointCloudPtr = typename PointCloud::Ptr;
78 
79  // Boost shared pointers
83 
87 
88  /** \brief Octree constructor.
89  * \param[in] resolution octree resolution at lowest octree level
90  */
91  Octree (const double resolution)
92  : Search<PointT> ("Octree")
93  , tree_ (new pcl::octree::OctreePointCloudSearch<PointT, LeafTWrap, BranchTWrap> (resolution))
94  {
95  }
96 
97  /** \brief Empty Destructor. */
98 
99  ~Octree () override = default;
100 
101  /** \brief Provide a pointer to the input dataset.
102  * \param[in] cloud the const boost shared pointer to a PointCloud message
103  */
104  inline void
106  {
107  tree_->deleteTree ();
108  tree_->setInputCloud (cloud);
109  tree_->addPointsFromInputCloud ();
110  input_ = cloud;
111  }
112 
113  /** \brief Provide a pointer to the input dataset.
114  * \param[in] cloud the const boost shared pointer to a PointCloud message
115  * \param[in] indices the point indices subset that is to be used from \a cloud
116  */
117  inline void
118  setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr& indices) override
119  {
120  tree_->deleteTree ();
121  tree_->setInputCloud (cloud, indices);
122  tree_->addPointsFromInputCloud ();
123  input_ = cloud;
124  indices_ = indices;
125  }
126 
127  /** \brief Search for the k-nearest neighbors for the given query point.
128  * \param[in] cloud the point cloud data
129  * \param[in] index the index in \a cloud representing the query point
130  * \param[in] k the number of neighbors to search for
131  * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
132  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
133  * a priori!)
134  * \return number of neighbors found
135  */
136  inline int
137  nearestKSearch (const PointCloud &cloud, index_t index, int k, Indices &k_indices,
138  std::vector<float> &k_sqr_distances) const override
139  {
140  return (tree_->nearestKSearch (cloud, index, k, k_indices, k_sqr_distances));
141  }
142 
143  /** \brief Search for the k-nearest neighbors for the given query point.
144  * \param[in] point the given query point
145  * \param[in] k the number of neighbors to search for
146  * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
147  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
148  * a priori!)
149  * \return number of neighbors found
150  */
151  inline int
152  nearestKSearch (const PointT &point, int k, Indices &k_indices,
153  std::vector<float> &k_sqr_distances) const override
154  {
155  return (tree_->nearestKSearch (point, k, k_indices, k_sqr_distances));
156  }
157 
158  /** \brief Search for the k-nearest neighbors for the given query point (zero-copy).
159  *
160  * \param[in] index the index representing the query point in the
161  * dataset given by \a setInputCloud if indices were given in
162  * setInputCloud, index will be the position in the indices vector
163  * \param[in] k the number of neighbors to search for
164  * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
165  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
166  * a priori!)
167  * \return number of neighbors found
168  */
169  inline int
170  nearestKSearch (index_t index, int k, Indices &k_indices, std::vector<float> &k_sqr_distances) const override
171  {
172  return (tree_->nearestKSearch (index, k, k_indices, k_sqr_distances));
173  }
174 
175  /** \brief search for all neighbors of query point that are within a given radius.
176  * \param cloud the point cloud data
177  * \param index the index in \a cloud representing the query point
178  * \param radius the radius of the sphere bounding all of p_q's neighbors
179  * \param k_indices the resultant indices of the neighboring points
180  * \param k_sqr_distances the resultant squared distances to the neighboring points
181  * \param max_nn if given, bounds the maximum returned neighbors to this value
182  * \return number of neighbors found in radius
183  */
184  inline int
185  radiusSearch (const PointCloud &cloud,
186  index_t index,
187  double radius,
188  Indices &k_indices,
189  std::vector<float> &k_sqr_distances,
190  unsigned int max_nn = 0) const override
191  {
192  tree_->radiusSearch (cloud, index, radius, k_indices, k_sqr_distances, max_nn);
193  if (sorted_results_)
194  this->sortResults (k_indices, k_sqr_distances);
195  return (static_cast<int> (k_indices.size ()));
196  }
197 
198  /** \brief search for all neighbors of query point that are within a given radius.
199  * \param p_q the given query point
200  * \param radius the radius of the sphere bounding all of p_q's neighbors
201  * \param k_indices the resultant indices of the neighboring points
202  * \param k_sqr_distances the resultant squared distances to the neighboring points
203  * \param max_nn if given, bounds the maximum returned neighbors to this value
204  * \return number of neighbors found in radius
205  */
206  inline int
207  radiusSearch (const PointT &p_q,
208  double radius,
209  Indices &k_indices,
210  std::vector<float> &k_sqr_distances,
211  unsigned int max_nn = 0) const override
212  {
213  tree_->radiusSearch (p_q, radius, k_indices, k_sqr_distances, max_nn);
214  if (sorted_results_)
215  this->sortResults (k_indices, k_sqr_distances);
216  return (static_cast<int> (k_indices.size ()));
217  }
218 
219  /** \brief search for all neighbors of query point that are within a given radius.
220  * \param index index representing the query point in the dataset given by \a setInputCloud.
221  * If indices were given in setInputCloud, index will be the position in the indices vector
222  * \param radius radius of the sphere bounding all of p_q's neighbors
223  * \param k_indices the resultant indices of the neighboring points
224  * \param k_sqr_distances the resultant squared distances to the neighboring points
225  * \param max_nn if given, bounds the maximum returned neighbors to this value
226  * \return number of neighbors found in radius
227  */
228  inline int
229  radiusSearch (index_t index, double radius, Indices &k_indices,
230  std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const override
231  {
232  tree_->radiusSearch (index, radius, k_indices, k_sqr_distances, max_nn);
233  if (sorted_results_)
234  this->sortResults (k_indices, k_sqr_distances);
235  return (static_cast<int> (k_indices.size ()));
236  }
237 
238 
239  /** \brief Search for approximate nearest neighbor at the query point.
240  * \param[in] cloud the point cloud data
241  * \param[in] query_index the index in \a cloud representing the query point
242  * \param[out] result_index the resultant index of the neighbor point
243  * \param[out] sqr_distance the resultant squared distance to the neighboring point
244  * \return number of neighbors found
245  */
246  inline void
247  approxNearestSearch (const PointCloudConstPtr &cloud, index_t query_index, index_t &result_index,
248  float &sqr_distance)
249  {
250  return (tree_->approxNearestSearch ((*cloud)[query_index], result_index, sqr_distance));
251  }
252 
253  /** \brief Search for approximate nearest neighbor at the query point.
254  * \param[in] p_q the given query point
255  * \param[out] result_index the resultant index of the neighbor point
256  * \param[out] sqr_distance the resultant squared distance to the neighboring point
257  */
258  inline void
259  approxNearestSearch (const PointT &p_q, index_t &result_index, float &sqr_distance)
260  {
261  return (tree_->approxNearestSearch (p_q, result_index, sqr_distance));
262  }
263 
264  /** \brief Search for approximate nearest neighbor at the query point.
265  * \param query_index index representing the query point in the dataset given by \a setInputCloud.
266  * If indices were given in setInputCloud, index will be the position in the indices vector.
267  * \param result_index the resultant index of the neighbor point
268  * \param sqr_distance the resultant squared distance to the neighboring point
269  * \return number of neighbors found
270  */
271  inline void
272  approxNearestSearch (index_t query_index, index_t &result_index, float &sqr_distance)
273  {
274  return (tree_->approxNearestSearch (query_index, result_index, sqr_distance));
275  }
276  /** \brief Search for points within rectangular search area
277  * \param[in] min_pt lower corner of search area
278  * \param[in] max_pt upper corner of search area
279  * \param[out] k_indices the resultant point indices
280  * \return number of points found within search area
281  */
282  inline uindex_t
283  boxSearch(const Eigen::Vector3f &min_pt, const Eigen::Vector3f &max_pt, Indices &k_indices) const
284  {
285  return (tree_->boxSearch(min_pt, max_pt, k_indices));
286  }
287  };
288  }
289 }
290 
291 #ifdef PCL_NO_PRECOMPILE
292 #include <pcl/octree/impl/octree_search.hpp>
293 #else
294 #define PCL_INSTANTIATE_Octree(T) template class PCL_EXPORTS pcl::search::Octree<T>;
295 #endif
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:413
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:414
Octree container class that does not store any information.
Octree container class that does store a vector of point indices.
shared_ptr< const OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT > > ConstPtr
Definition: octree_search.h:72
shared_ptr< OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT > > Ptr
Definition: octree_search.h:70
search::Octree is a wrapper class which implements nearest neighbor search operations based on the pc...
Definition: octree.h:69
shared_ptr< const pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT > > ConstPtr
Definition: octree.h:73
void setInputCloud(const PointCloudConstPtr &cloud, const IndicesConstPtr &indices) override
Provide a pointer to the input dataset.
Definition: octree.h:118
uindex_t boxSearch(const Eigen::Vector3f &min_pt, const Eigen::Vector3f &max_pt, Indices &k_indices) const
Search for points within rectangular search area.
Definition: octree.h:283
typename PointCloud::Ptr PointCloudPtr
Definition: octree.h:76
int nearestKSearch(index_t index, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
Search for the k-nearest neighbors for the given query point (zero-copy).
Definition: octree.h:170
void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition: octree.h:105
int radiusSearch(const PointT &p_q, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const override
search for all neighbors of query point that are within a given radius.
Definition: octree.h:207
OctreePointCloudSearchPtr tree_
Definition: octree.h:82
void approxNearestSearch(const PointCloudConstPtr &cloud, index_t query_index, index_t &result_index, float &sqr_distance)
Search for approximate nearest neighbor at the query point.
Definition: octree.h:247
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: octree.h:77
int radiusSearch(index_t index, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const override
search for all neighbors of query point that are within a given radius.
Definition: octree.h:229
typename pcl::octree::OctreePointCloudSearch< PointT, LeafTWrap, BranchTWrap >::ConstPtr OctreePointCloudSearchConstPtr
Definition: octree.h:81
void approxNearestSearch(index_t query_index, index_t &result_index, float &sqr_distance)
Search for approximate nearest neighbor at the query point.
Definition: octree.h:272
Octree(const double resolution)
Octree constructor.
Definition: octree.h:91
int radiusSearch(const PointCloud &cloud, index_t index, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const override
search for all neighbors of query point that are within a given radius.
Definition: octree.h:185
typename pcl::octree::OctreePointCloudSearch< PointT, LeafTWrap, BranchTWrap >::Ptr OctreePointCloudSearchPtr
Definition: octree.h:80
shared_ptr< pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT > > Ptr
Definition: octree.h:72
void approxNearestSearch(const PointT &p_q, index_t &result_index, float &sqr_distance)
Search for approximate nearest neighbor at the query point.
Definition: octree.h:259
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: octree.h:152
~Octree() override=default
Empty Destructor.
int nearestKSearch(const PointCloud &cloud, index_t index, 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: octree.h:137
Generic search class.
Definition: search.h:75
PointCloudConstPtr input_
Definition: search.h:401
void sortResults(Indices &indices, std::vector< float > &distances) const
Definition: search.hpp:188
IndicesConstPtr indices_
Definition: search.h:402
pcl::IndicesConstPtr IndicesConstPtr
Definition: search.h:85
bool sorted_results_
Definition: search.h:403
detail::int_type_t< detail::index_type_size, false > uindex_t
Type used for an unsigned index in PCL.
Definition: types.h:120
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition: types.h:112
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
A point structure representing Euclidean xyz coordinates, and the RGB color.