Point Cloud Library (PCL)  1.11.1-dev
organized.h
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39 
40 #pragma once
41 
42 #include <pcl/memory.h>
43 #include <pcl/pcl_macros.h>
44 #include <pcl/point_cloud.h>
45 #include <pcl/search/search.h>
46 #include <pcl/common/eigen.h>
47 
48 #include <algorithm>
49 #include <vector>
50 
51 namespace pcl
52 {
53  namespace search
54  {
55  /** \brief OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds.
56  * \author Radu B. Rusu, Julius Kammerl, Suat Gedikli, Koen Buys
57  * \ingroup search
58  */
59  template<typename PointT>
60  class OrganizedNeighbor : public pcl::search::Search<PointT>
61  {
62 
63  public:
64  // public typedefs
66  using PointCloudPtr = typename PointCloud::Ptr;
67 
69 
70  using Ptr = shared_ptr<pcl::search::OrganizedNeighbor<PointT> >;
71  using ConstPtr = shared_ptr<const pcl::search::OrganizedNeighbor<PointT> >;
72 
76 
77  /** \brief Constructor
78  * \param[in] sorted_results whether the results should be return sorted in ascending order on the distances or not.
79  * This applies only for radius search, since knn always returns sorted resutls
80  * \param[in] eps the threshold for the mean-squared-error of the estimation of the projection matrix.
81  * if the MSE is above this value, the point cloud is considered as not from a projective device,
82  * thus organized neighbor search can not be applied on that cloud.
83  * \param[in] pyramid_level the level of the down sampled point cloud to be used for projection matrix estimation
84  */
85  OrganizedNeighbor (bool sorted_results = false, float eps = 1e-4f, unsigned pyramid_level = 5)
86  : Search<PointT> ("OrganizedNeighbor", sorted_results)
87  , projection_matrix_ (Eigen::Matrix<float, 3, 4, Eigen::RowMajor>::Zero ())
88  , KR_ (Eigen::Matrix<float, 3, 3, Eigen::RowMajor>::Zero ())
89  , KR_KRT_ (Eigen::Matrix<float, 3, 3, Eigen::RowMajor>::Zero ())
90  , eps_ (eps)
91  , pyramid_level_ (pyramid_level)
92  {
93  }
94 
95  /** \brief Empty deconstructor. */
97 
98  /** \brief Test whether this search-object is valid (input is organized AND from projective device)
99  * User should use this method after setting the input cloud, since setInput just prints an error
100  * if input is not organized or a projection matrix could not be determined.
101  * \return true if the input data is organized and from a projective device, false otherwise
102  */
103  bool
104  isValid () const
105  {
106  // determinant (KR) = determinant (K) * determinant (R) = determinant (K) = f_x * f_y.
107  // If we expect at max an opening angle of 170degree in x-direction -> f_x = 2.0 * width / tan (85 degree);
108  // 2 * tan (85 degree) ~ 22.86
109  float min_f = 0.043744332f * static_cast<float>(input_->width);
110  //std::cout << "isValid: " << determinant3x3Matrix<Eigen::Matrix3f> (KR_ / sqrt (KR_KRT_.coeff (8))) << " >= " << (min_f * min_f) << std::endl;
111  return (determinant3x3Matrix<Eigen::Matrix3f> (KR_ / std::sqrt (KR_KRT_.coeff (8))) >= (min_f * min_f));
112  }
113 
114  /** \brief Compute the camera matrix
115  * \param[out] camera_matrix the resultant computed camera matrix
116  */
117  void
118  computeCameraMatrix (Eigen::Matrix3f& camera_matrix) const;
119 
120  /** \brief Provide a pointer to the input data set, if user has focal length he must set it before calling this
121  * \param[in] cloud the const boost shared pointer to a PointCloud message
122  * \param[in] indices the const boost shared pointer to PointIndices
123  */
124  void
125  setInputCloud (const PointCloudConstPtr& cloud, const IndicesConstPtr &indices = IndicesConstPtr ()) override
126  {
127  input_ = cloud;
128 
129  mask_.resize (input_->size ());
130  input_ = cloud;
131  indices_ = indices;
132 
133  if (indices_ && !indices_->empty())
134  {
135  mask_.assign (input_->size (), 0);
136  for (const auto& idx : *indices_)
137  mask_[idx] = 1;
138  }
139  else
140  mask_.assign (input_->size (), 1);
141 
143  }
144 
145  /** \brief Search for all neighbors of query point that are within a given radius.
146  * \param[in] p_q the given query point
147  * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
148  * \param[out] k_indices the resultant indices of the neighboring points
149  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
150  * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
151  * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
152  * returned.
153  * \return number of neighbors found in radius
154  */
155  int
156  radiusSearch (const PointT &p_q,
157  double radius,
158  Indices &k_indices,
159  std::vector<float> &k_sqr_distances,
160  unsigned int max_nn = 0) const override;
161 
162  /** \brief estimated the projection matrix from the input cloud. */
163  void
165 
166  /** \brief Search for the k-nearest neighbors for a given query point.
167  * \note limiting the maximum search radius (with setMaxDistance) can lead to a significant improvement in search speed
168  * \param[in] p_q the given query point (\ref setInputCloud must be given a-priori!)
169  * \param[in] k the number of neighbors to search for (used only if horizontal and vertical window not given already!)
170  * \param[out] k_indices the resultant point indices (must be resized to \a k beforehand!)
171  * \param[out] k_sqr_distances \note this function does not return distances
172  * \return number of neighbors found
173  * @todo still need to implements this functionality
174  */
175  int
176  nearestKSearch (const PointT &p_q,
177  int k,
178  Indices &k_indices,
179  std::vector<float> &k_sqr_distances) const override;
180 
181  /** \brief projects a point into the image
182  * \param[in] p point in 3D World Coordinate Frame to be projected onto the image plane
183  * \param[out] q the 2D projected point in pixel coordinates (u,v)
184  * @return true if projection is valid, false otherwise
185  */
186  bool projectPoint (const PointT& p, pcl::PointXY& q) const;
187 
188  protected:
189 
190  struct Entry
191  {
192  Entry (index_t idx, float dist) : index (idx), distance (dist) {}
193  Entry () : index (0), distance (0) {}
195  float distance;
196 
197  inline bool
198  operator < (const Entry& other) const
199  {
200  return (distance < other.distance);
201  }
202  };
203 
204  /** \brief test if point given by index is among the k NN in results to the query point.
205  * \param[in] query query point
206  * \param[in] k number of maximum nn interested in
207  * \param[in,out] queue priority queue with k NN
208  * \param[in] index index on point to be tested
209  * \return whether the top element changed or not.
210  */
211  inline bool
212  testPoint (const PointT& query, unsigned k, std::vector<Entry>& queue, index_t index) const
213  {
214  const PointT& point = input_->points [index];
215  if (mask_ [index] && std::isfinite (point.x))
216  {
217  //float squared_distance = (point.getVector3fMap () - query.getVector3fMap ()).squaredNorm ();
218  float dist_x = point.x - query.x;
219  float dist_y = point.y - query.y;
220  float dist_z = point.z - query.z;
221  float squared_distance = dist_x * dist_x + dist_y * dist_y + dist_z * dist_z;
222  const auto queue_size = queue.size ();
223  const auto insert_into_queue = [&]{ queue.emplace (
224  std::upper_bound (queue.begin(), queue.end(), squared_distance,
225  [](float dist, const Entry& ent){ return dist<ent.distance; }),
226  index, squared_distance); };
227  if (queue_size < k)
228  {
229  insert_into_queue ();
230  return (queue_size + 1) == k;
231  }
232  if (queue.back ().distance > squared_distance)
233  {
234  queue.pop_back ();
235  insert_into_queue ();
236  return true; // top element has changed!
237  }
238  }
239  return false;
240  }
241 
242  inline void
243  clipRange (int& begin, int &end, int min, int max) const
244  {
245  begin = std::max (std::min (begin, max), min);
246  end = std::min (std::max (end, min), max);
247  }
248 
249  /** \brief Obtain a search box in 2D from a sphere with a radius in 3D
250  * \param[in] point the query point (sphere center)
251  * \param[in] squared_radius the squared sphere radius
252  * \param[out] minX the min X box coordinate
253  * \param[out] minY the min Y box coordinate
254  * \param[out] maxX the max X box coordinate
255  * \param[out] maxY the max Y box coordinate
256  */
257  void
258  getProjectedRadiusSearchBox (const PointT& point, float squared_radius, unsigned& minX, unsigned& minY,
259  unsigned& maxX, unsigned& maxY) const;
260 
261 
262  /** \brief the projection matrix. Either set by user or calculated by the first / each input cloud */
263  Eigen::Matrix<float, 3, 4, Eigen::RowMajor> projection_matrix_;
264 
265  /** \brief inveser of the left 3x3 projection matrix which is K * R (with K being the camera matrix and R the rotation matrix)*/
266  Eigen::Matrix<float, 3, 3, Eigen::RowMajor> KR_;
267 
268  /** \brief inveser of the left 3x3 projection matrix which is K * R (with K being the camera matrix and R the rotation matrix)*/
269  Eigen::Matrix<float, 3, 3, Eigen::RowMajor> KR_KRT_;
270 
271  /** \brief epsilon value for the MSE of the projection matrix estimation*/
272  const float eps_;
273 
274  /** \brief using only a subsample of points to calculate the projection matrix. pyramid_level_ = use down sampled cloud given by pyramid_level_*/
275  const unsigned pyramid_level_;
276 
277  /** \brief mask, indicating whether the point was in the indices list or not.*/
278  std::vector<unsigned char> mask_;
279  public:
281  };
282  }
283 }
284 
285 #ifdef PCL_NO_PRECOMPILE
286 #include <pcl/search/impl/organized.hpp>
287 #endif
pcl::search::Search
Generic search class.
Definition: search.h:74
pcl_macros.h
Defines all the PCL and non-PCL macros used.
pcl
Definition: convolution.h:46
pcl::search::OrganizedNeighbor::KR_
Eigen::Matrix< float, 3, 3, Eigen::RowMajor > KR_
inveser of the left 3x3 projection matrix which is K * R (with K being the camera matrix and R the ro...
Definition: organized.h:266
Eigen
Definition: bfgs.h:10
pcl::search::OrganizedNeighbor::Entry::operator<
bool operator<(const Entry &other) const
Definition: organized.h:198
pcl::search::Search::IndicesConstPtr
pcl::IndicesConstPtr IndicesConstPtr
Definition: search.h:85
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: distances.h:55
pcl::index_t
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition: types.h:110
pcl::search::OrganizedNeighbor::~OrganizedNeighbor
~OrganizedNeighbor()
Empty deconstructor.
Definition: organized.h:96
pcl::search::OrganizedNeighbor::nearestKSearch
int nearestKSearch(const PointT &p_q, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
Search for the k-nearest neighbors for a given query point.
Definition: organized.hpp:114
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:628
pcl::search::OrganizedNeighbor::projectPoint
bool projectPoint(const PointT &p, pcl::PointXY &q) const
projects a point into the image
Definition: organized.hpp:378
pcl::search::OrganizedNeighbor::PointCloudConstPtr
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: organized.h:68
pcl::search::OrganizedNeighbor::projection_matrix_
Eigen::Matrix< float, 3, 4, Eigen::RowMajor > projection_matrix_
the projection matrix.
Definition: organized.h:263
pcl::search::OrganizedNeighbor::mask_
std::vector< unsigned char > mask_
mask, indicating whether the point was in the indices list or not.
Definition: organized.h:278
pcl::search::OrganizedNeighbor::Entry::Entry
Entry(index_t idx, float dist)
Definition: organized.h:192
pcl::search::OrganizedNeighbor::pyramid_level_
const unsigned pyramid_level_
using only a subsample of points to calculate the projection matrix.
Definition: organized.h:275
pcl::search::OrganizedNeighbor::KR_KRT_
Eigen::Matrix< float, 3, 3, Eigen::RowMajor > KR_KRT_
inveser of the left 3x3 projection matrix which is K * R (with K being the camera matrix and R the ro...
Definition: organized.h:269
pcl::search::OrganizedNeighbor::testPoint
bool testPoint(const PointT &query, unsigned k, std::vector< Entry > &queue, index_t index) const
test if point given by index is among the k NN in results to the query point.
Definition: organized.h:212
pcl::search::OrganizedNeighbor::eps_
const float eps_
epsilon value for the MSE of the projection matrix estimation
Definition: organized.h:272
pcl::search::OrganizedNeighbor::Entry::Entry
Entry()
Definition: organized.h:193
pcl::search::Search::input_
PointCloudConstPtr input_
Definition: search.h:403
pcl::search::OrganizedNeighbor::OrganizedNeighbor
OrganizedNeighbor(bool sorted_results=false, float eps=1e-4f, unsigned pyramid_level=5)
Constructor.
Definition: organized.h:85
pcl::search::OrganizedNeighbor::Entry
Definition: organized.h:190
pcl::search::OrganizedNeighbor::estimateProjectionMatrix
void estimateProjectionMatrix()
estimated the projection matrix from the input cloud.
Definition: organized.hpp:333
PCL_MAKE_ALIGNED_OPERATOR_NEW
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
pcl::search::OrganizedNeighbor::Entry::index
index_t index
Definition: organized.h:194
pcl::PointXY
A 2D point structure representing Euclidean xy coordinates.
Definition: point_types.hpp:744
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:131
pcl::search::OrganizedNeighbor::radiusSearch
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: organized.hpp:49
pcl::search::OrganizedNeighbor
OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds.
Definition: organized.h:60
pcl::search::OrganizedNeighbor::Ptr
shared_ptr< pcl::search::OrganizedNeighbor< PointT > > Ptr
Definition: organized.h:70
pcl::PointCloud::Ptr
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:407
pcl::search::OrganizedNeighbor::isValid
bool isValid() const
Test whether this search-object is valid (input is organized AND from projective device) User should ...
Definition: organized.h:104
pcl::search::Search::indices_
IndicesConstPtr indices_
Definition: search.h:404
pcl::search::OrganizedNeighbor::clipRange
void clipRange(int &begin, int &end, int min, int max) const
Definition: organized.h:243
pcl::search::OrganizedNeighbor::PointCloudPtr
typename PointCloud::Ptr PointCloudPtr
Definition: organized.h:66
pcl::search::OrganizedNeighbor::getProjectedRadiusSearchBox
void getProjectedRadiusSearchBox(const PointT &point, float squared_radius, unsigned &minX, unsigned &minY, unsigned &maxX, unsigned &maxY) const
Obtain a search box in 2D from a sphere with a radius in 3D.
Definition: organized.hpp:269
pcl::PointCloud::ConstPtr
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:408
pcl::search::OrganizedNeighbor::Entry::distance
float distance
Definition: organized.h:195
pcl::search::OrganizedNeighbor::computeCameraMatrix
void computeCameraMatrix(Eigen::Matrix3f &camera_matrix) const
Compute the camera matrix.
Definition: organized.hpp:326
pcl::search::OrganizedNeighbor::ConstPtr
shared_ptr< const pcl::search::OrganizedNeighbor< PointT > > ConstPtr
Definition: organized.h:71
pcl::search::OrganizedNeighbor::setInputCloud
void setInputCloud(const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override
Provide a pointer to the input data set, if user has focal length he must set it before calling this.
Definition: organized.h:125
memory.h
Defines functions, macros and traits for allocating and using memory.