Point Cloud Library (PCL)  1.13.0-dev
flare.h
1 /*
3 *
4 * Point Cloud Library (PCL) - www.pointclouds.org
5 * Copyright (c) 2016-, Open Perception, Inc.
6 *
8 *
9 * Redistribution and use in source and binary forms, with or without
10 * modification, are permitted provided that the following conditions
11 * are met:
12 *
13 * * Redistributions of source code must retain the above copyright
14 * notice, this list of conditions and the following disclaimer.
15 * * Redistributions in binary form must reproduce the above
16 * copyright notice, this list of conditions and the following
17 * disclaimer in the documentation and/or other materials provided
18 * with the distribution.
19 * * Neither the name of the copyright holder(s) nor the names of its
20 * contributors may be used to endorse or promote products derived
21 * from this software without specific prior written permission.
22 *
23 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
24 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
25 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
26 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
27 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
28 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
29 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
30 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
31 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
32 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
33 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
34 * POSSIBILITY OF SUCH DAMAGE.
35 *
36 *
37 */
38
39 #pragma once
40
41 #include <pcl/point_types.h>
42 #include <pcl/features/feature.h>
43 #include <pcl/features/normal_3d.h>
44
45
46 namespace pcl
47 {
48
49  /** \brief FLARELocalReferenceFrameEstimation implements the Fast LocAl Reference framE algorithm
50  * for local reference frame estimation as described here:
51  *
52  * - A. Petrelli, L. Di Stefano,
53  * "A repeatable and efficient canonical reference for surface matching",
54  * 3DimPVT, 2012
55  *
56  * FLARE algorithm is deployed in ReLOC algorithm proposed in:
57  *
58  * Petrelli A., Di Stefano L., "Pairwise registration by local orientation cues", Computer Graphics Forum, 2015.
59  *
60  * \author Alioscia Petrelli
61  * \ingroup features
62  */
63  template<typename PointInT, typename PointNT, typename PointOutT = ReferenceFrame, typename SignedDistanceT = float>
64  class FLARELocalReferenceFrameEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
65  {
66  protected:
76
79
81
83
86
87  using Ptr = shared_ptr<FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT> >;
88  using ConstPtr = shared_ptr<const FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT> >;
89
90  public:
91  /** \brief Constructor. */
94  margin_thresh_ (0.85f),
95  min_neighbors_for_normal_axis_ (6),
96  min_neighbors_for_tangent_axis_ (6),
97  sampled_surface_ (),
98  sampled_tree_ (),
99  fake_sampled_surface_ (false)
100  {
101  feature_name_ = "FLARELocalReferenceFrameEstimation";
102  }
103
104  //Getters/Setters
105
106  /** \brief Set the maximum distance of the points used to estimate the x_axis of the FLARE Reference Frame for a given point.
107  *
109  */
110  inline void
112  {
114  }
115
116  /** \brief Get the maximum distance of the points used to estimate the x_axis of the FLARE Reference Frame for a given point.
117  *
118  * \return The search radius for x axis.
119  */
120  inline float
122  {
124  }
125
126  /** \brief Set the percentage of the search tangent radius after which a point is considered part of the support.
127  *
128  * \param[in] margin_thresh the percentage of the search tangent radius after which a point is considered part of the support.
129  */
130  inline void
131  setMarginThresh (float margin_thresh)
132  {
133  margin_thresh_ = margin_thresh;
134  }
135
136  /** \brief Get the percentage of the search tangent radius after which a point is considered part of the support.
137  *
138  * \return The percentage of the search tangent radius after which a point is considered part of the support.
139  */
140  inline float
142  {
143  return (margin_thresh_);
144  }
145
146
147  /** \brief Set min number of neighbours required for the computation of Z axis.
148  *
149  * \param[in] min_neighbors_for_normal_axis min number of neighbours required for the computation of Z axis.
150  */
151  inline void
152  setMinNeighboursForNormalAxis (int min_neighbors_for_normal_axis)
153  {
154  min_neighbors_for_normal_axis_ = min_neighbors_for_normal_axis;
155  }
156
157  /** \brief Get min number of neighbours required for the computation of Z axis.
158  *
159  * \return min number of neighbours required for the computation of Z axis.
160  */
161  inline int
163  {
164  return (min_neighbors_for_normal_axis_);
165  }
166
167
168  /** \brief Set min number of neighbours required for the computation of X axis.
169  *
170  * \param[in] min_neighbors_for_tangent_axis min number of neighbours required for the computation of X axis.
171  */
172  inline void
173  setMinNeighboursForTangentAxis (int min_neighbors_for_tangent_axis)
174  {
175  min_neighbors_for_tangent_axis_ = min_neighbors_for_tangent_axis;
176  }
177
178  /** \brief Get min number of neighbours required for the computation of X axis.
179  *
180  * \return min number of neighbours required for the computation of X axis.
181  */
182  inline int
184  {
185  return (min_neighbors_for_tangent_axis_);
186  }
187
188
189  /** \brief Provide a pointer to the dataset used for the estimation of X axis.
190  * As the estimation of x axis is negligibly affected by surface downsampling,
191  * this method lets to consider a downsampled version of surface_ in the estimation of x axis.
192  * This is optional, if this is not set, it will only use the data in the
193  * surface_ cloud to estimate the x axis.
194  * \param[in] cloud a pointer to a PointCloud
195  */
196  inline void
198  {
199  sampled_surface_ = cloud;
200  fake_sampled_surface_ = false;
201  }
202
203  /** \brief Get a pointer to the sampled_surface_ cloud dataset. */
204  inline const PointCloudInConstPtr&
206  {
207  return (sampled_surface_);
208  }
209
210  /** \brief Provide a pointer to the search object linked to sampled_surface.
211  * \param[in] tree a pointer to the spatial search object linked to sampled_surface.
212  */
213  inline void
214  setSearchMethodForSampledSurface (const KdTreePtr &tree) { sampled_tree_ = tree; }
215
216  /** \brief Get a pointer to the search method used for the estimation of x axis. */
217  inline const KdTreePtr&
219  {
220  return (sampled_tree_);
221  }
222
223  /** \brief Get the signed distances of the highest points from the fitted planes. */
224  inline const std::vector<SignedDistanceT> &
226  {
227  return (signed_distances_from_highest_points_);
228  }
229
230  protected:
231  /** \brief This method should get called before starting the actual computation. */
232  bool
233  initCompute () override;
234
235  /** \brief This method should get called after the actual computation is ended. */
236  bool
237  deinitCompute () override;
238
239  /** \brief Estimate the LRF descriptor for a given point based on its spatial neighborhood of 3D points with normals
240  * \param[in] index the index of the point in input_
241  * \param[out] lrf the resultant local reference frame
242  * \return signed distance of the highest point from the fitted plane. Max if the lrf is not computable.
243  */
244  SignedDistanceT
245  computePointLRF (const int index, Eigen::Matrix3f &lrf);
246
247  /** \brief Abstract feature estimation method.
248  * \param[out] output the resultant features
249  */
250  void
251  computeFeature (PointCloudOut &output) override;
252
253
254  private:
255  /** \brief Radius used to find tangent axis. */
257
258  /** \brief Threshold that define if a support point is near the margins. */
259  float margin_thresh_;
260
261  /** \brief Min number of neighbours required for the computation of Z axis. Otherwise, feature point normal is used. */
262  int min_neighbors_for_normal_axis_;
263
264  /** \brief Min number of neighbours required for the computation of X axis. Otherwise, a random X axis is set */
265  int min_neighbors_for_tangent_axis_;
266
267  /** \brief An input point cloud describing the surface that is to be used
268  * for nearest neighbor searches for the estimation of X axis.
269  */
270  PointCloudInConstPtr sampled_surface_;
271
272  /** \brief A pointer to the spatial search object used for the estimation of X axis. */
273  KdTreePtr sampled_tree_;
274
275  /** \brief Class for normal estimation. */
276  NormalEstimation<PointInT, PointNT> normal_estimation_;
277
278  /** \brief Signed distances of the highest points from the fitted planes.*/
279  std::vector<SignedDistanceT> signed_distances_from_highest_points_;
280
281  /** \brief If no sampled_surface_ is given, we use surface_ as the sampled surface. */
282  bool fake_sampled_surface_;
283
284  };
285
286 }
287
288 #ifdef PCL_NO_PRECOMPILE
289 #include <pcl/features/impl/flare.hpp>
290 #endif
FLARELocalReferenceFrameEstimation implements the Fast LocAl Reference framE algorithm for local refe...
Definition: flare.h:65
FLARELocalReferenceFrameEstimation()
Constructor.
Definition: flare.h:92
int getMinNeighboursForNormalAxis() const
Get min number of neighbours required for the computation of Z axis.
Definition: flare.h:162
void setMarginThresh(float margin_thresh)
Set the percentage of the search tangent radius after which a point is considered part of the support...
Definition: flare.h:131
const std::vector< SignedDistanceT > & getSignedDistancesFromHighestPoints() const
Get the signed distances of the highest points from the fitted planes.
Definition: flare.h:225
const PointCloudInConstPtr & getSearchSampledSurface() const
Get a pointer to the sampled_surface_ cloud dataset.
Definition: flare.h:205
bool deinitCompute() override
This method should get called after the actual computation is ended.
Definition: flare.hpp:91
void setSearchMethodForSampledSurface(const KdTreePtr &tree)
Provide a pointer to the search object linked to sampled_surface.
Definition: flare.h:214
shared_ptr< const FLARELocalReferenceFrameEstimation< PointInT, PointNT, PointOutT > > ConstPtr
Definition: flare.h:88
void computeFeature(PointCloudOut &output) override
Abstract feature estimation method.
Definition: flare.hpp:233
float getMarginThresh() const
Get the percentage of the search tangent radius after which a point is considered part of the support...
Definition: flare.h:141
Set the maximum distance of the points used to estimate the x_axis of the FLARE Reference Frame for a...
Definition: flare.h:111
Get the maximum distance of the points used to estimate the x_axis of the FLARE Reference Frame for a...
Definition: flare.h:121
int getMinNeighboursForTangentAxis() const
Get min number of neighbours required for the computation of X axis.
Definition: flare.h:183
bool initCompute() override
This method should get called before starting the actual computation.
Definition: flare.hpp:47
SignedDistanceT computePointLRF(const int index, Eigen::Matrix3f &lrf)
Estimate the LRF descriptor for a given point based on its spatial neighborhood of 3D points with nor...
Definition: flare.hpp:110
void setMinNeighboursForNormalAxis(int min_neighbors_for_normal_axis)
Set min number of neighbours required for the computation of Z axis.
Definition: flare.h:152
void setMinNeighboursForTangentAxis(int min_neighbors_for_tangent_axis)
Set min number of neighbours required for the computation of X axis.
Definition: flare.h:173
const KdTreePtr & getSearchMethodForSampledSurface() const
Get a pointer to the search method used for the estimation of x axis.
Definition: flare.h:218
void setSearchSampledSurface(const PointCloudInConstPtr &cloud)
Provide a pointer to the dataset used for the estimation of X axis.
Definition: flare.h:197
typename PointCloudSignedDistance::Ptr PointCloudSignedDistancePtr
Definition: flare.h:85
shared_ptr< FLARELocalReferenceFrameEstimation< PointInT, PointNT, PointOutT > > Ptr
Definition: flare.h:87
Feature represents the base feature class.
Definition: feature.h:107
std::string feature_name_
The feature name.
Definition: feature.h:220
typename KdTree::Ptr KdTreePtr
Definition: feature.h:118
typename PointCloudIn::ConstPtr PointCloudInConstPtr
Definition: feature.h:122
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:413
Defines all the PCL implemented PointT point type structures.