Point Cloud Library (PCL)  1.12.1-dev
rops_estimation.h
1 /*
2  * Software License Agreement (BSD License)
3  *
4  * Point Cloud Library (PCL) - www.pointclouds.org
5  *
6  * All rights reserved.
7  *
8  * Redistribution and use in source and binary forms, with or without
9  * modification, are permitted provided that the following conditions
10  * are met:
11  *
12  * * Redistributions of source code must retain the above copyright
13  * notice, this list of conditions and the following disclaimer.
14  * * Redistributions in binary form must reproduce the above
15  * copyright notice, this list of conditions and the following
16  * disclaimer in the documentation and/or other materials provided
17  * with the distribution.
18  * * Neither the name of Willow Garage, Inc. nor the names of its
19  * contributors may be used to endorse or promote products derived
20  * from this software without specific prior written permission.
21  *
22  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
23  * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
24  * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
25  * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
26  * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
27  * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
28  * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
29  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
30  * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
31  * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
32  * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
33  * POSSIBILITY OF SUCH DAMAGE.
34  *
35  * Author : Sergey Ushakov
36  * Email : sergey.s.ushakov@mail.ru
37  *
38  */
39 
40 #pragma once
41 
42 #include <pcl/memory.h>
43 #include <pcl/pcl_macros.h>
44 #include <pcl/Vertices.h> // for Vertices
45 #include <pcl/features/feature.h>
46 #include <set>
47 
48 namespace pcl
49 {
50  /** \brief
51  * This class implements the method for extracting RoPS features presented in the article
52  * "Rotational Projection Statistics for 3D Local Surface Description and Object Recognition" by
53  * Yulan Guo, Ferdous Sohel, Mohammed Bennamoun, Min Lu and Jianwei Wan.
54  */
55  template <typename PointInT, typename PointOutT>
56  class PCL_EXPORTS ROPSEstimation : public pcl::Feature <PointInT, PointOutT>
57  {
58  public:
59 
64 
67 
68  public:
69 
70  /** \brief Simple constructor. */
71  ROPSEstimation ();
72 
73  /** \brief Virtual destructor. */
74 
75  ~ROPSEstimation () override;
76 
77  /** \brief Allows to set the number of partition bins that is used for distribution matrix calculation.
78  * \param[in] number_of_bins number of partition bins
79  */
80  void
81  setNumberOfPartitionBins (unsigned int number_of_bins);
82 
83  /** \brief Returns the number of partition bins. */
84  unsigned int
85  getNumberOfPartitionBins () const;
86 
87  /** \brief This method sets the number of rotations.
88  * \param[in] number_of_rotations number of rotations
89  */
90  void
91  setNumberOfRotations (unsigned int number_of_rotations);
92 
93  /** \brief returns the number of rotations. */
94  unsigned int
95  getNumberOfRotations () const;
96 
97  /** \brief Allows to set the support radius that is used to crop the local surface of the point.
98  * \param[in] support_radius support radius
99  */
100  void
101  setSupportRadius (float support_radius);
102 
103  /** \brief Returns the support radius. */
104  float
105  getSupportRadius () const;
106 
107  /** \brief This method sets the triangles of the mesh.
108  * \param[in] triangles list of triangles of the mesh
109  */
110  void
111  setTriangles (const std::vector <pcl::Vertices>& triangles);
112 
113  /** \brief Returns the triangles of the mesh.
114  * \param[out] triangles triangles of the mesh
115  */
116  void
117  getTriangles (std::vector <pcl::Vertices>& triangles) const;
118 
119  private:
120 
121  /** \brief Abstract feature estimation method.
122  * \param[out] output the resultant features
123  */
124  void
125  computeFeature (PointCloudOut& output) override;
126 
127  /** \brief This method simply builds the list of triangles for every point.
128  * The list of triangles for each point consists of indices of triangles it belongs to.
129  * The only purpose of this method is to improve performance of the algorithm.
130  */
131  void
132  buildListOfPointsTriangles ();
133 
134  /** \brief This method crops all the triangles within the given radius of the given point.
135  * \param[in] point point for which the local surface is computed
136  * \param[out] local_triangles stores the indices of the triangles that belong to the local surface
137  * \param[out] local_points stores the indices of the points that belong to the local surface
138  */
139  void
140  getLocalSurface (const PointInT& point, std::set <unsigned int>& local_triangles, pcl::Indices& local_points) const;
141 
142  /** \brief This method computes LRF (Local Reference Frame) matrix for the given point.
143  * \param[in] point point for which the LRF is computed
144  * \param[in] local_triangles list of triangles that represents the local surface of the point
145  * \paran[out] lrf_matrix stores computed LRF matrix for the given point
146  */
147  void
148  computeLRF (const PointInT& point, const std::set <unsigned int>& local_triangles, Eigen::Matrix3f& lrf_matrix) const;
149 
150  /** \brief This method calculates the eigen values and eigen vectors
151  * for the given covariance matrix. Note that it returns normalized eigen
152  * vectors that always form the right-handed coordinate system.
153  * \param[in] matrix covariance matrix of the cloud
154  * \param[out] major_axis eigen vector which corresponds to a major eigen value
155  * \param[out] middle_axis eigen vector which corresponds to a middle eigen value
156  * \param[out] minor_axis eigen vector which corresponds to a minor eigen value
157  */
158  void
159  computeEigenVectors (const Eigen::Matrix3f& matrix, Eigen::Vector3f& major_axis, Eigen::Vector3f& middle_axis,
160  Eigen::Vector3f& minor_axis) const;
161 
162  /** \brief This method translates the cloud so that the given point becomes the origin.
163  * After that the cloud is rotated with the help of the given matrix.
164  * \param[in] point point which stores the translation information
165  * \param[in] matrix rotation matrix
166  * \param[in] local_points point to transform
167  * \param[out] transformed_cloud stores the transformed cloud
168  */
169  void
170  transformCloud (const PointInT& point, const Eigen::Matrix3f& matrix, const pcl::Indices& local_points, PointCloudIn& transformed_cloud) const;
171 
172  /** \brief This method rotates the cloud around the given axis and computes AABB of the rotated cloud.
173  * \param[in] axis axis around which cloud must be rotated
174  * \param[in] angle angle in degrees
175  * \param[in] cloud cloud to rotate
176  * \param[out] rotated_cloud stores the rotated cloud
177  * \param[out] min stores the min point of the AABB
178  * \param[out] max stores the max point of the AABB
179  */
180  void
181  rotateCloud (const PointInT& axis, const float angle, const PointCloudIn& cloud, PointCloudIn& rotated_cloud,
182  Eigen::Vector3f& min, Eigen::Vector3f& max) const;
183 
184  /** \brief This method projects the local surface onto the XY, XZ or YZ plane
185  * and computes the distribution matrix.
186  * \param[in] projection represents the case of projection. 1 - XY, 2 - XZ, 3 - YZ
187  * \param[in] min min point of the AABB
188  * \param[in] max max point of the AABB
189  * \param[in] cloud cloud containing the points of the local surface
190  * \param[out] matrix stores computed distribution matrix
191  */
192  void
193  getDistributionMatrix (const unsigned int projection, const Eigen::Vector3f& min, const Eigen::Vector3f& max, const PointCloudIn& cloud, Eigen::MatrixXf& matrix) const;
194 
195  /** \brief This method computes the set ofcentral moments for the given matrix.
196  * \param[in] matrix input matrix
197  * \param[out] moments set of computed moments
198  */
199  void
200  computeCentralMoments (const Eigen::MatrixXf& matrix, std::vector <float>& moments) const;
201 
202  private:
203 
204  /** \brief Stores the number of partition bins that is used for distribution matrix calculation. */
205  unsigned int number_of_bins_;
206 
207  /** \brief Stores number of rotations. Central moments are calculated for every rotation. */
208  unsigned int number_of_rotations_;
209 
210  /** \brief Support radius that is used to crop the local surface of the point. */
211  float support_radius_;
212 
213  /** \brief Stores the squared support radius. Used to improve performance. */
214  float sqr_support_radius_;
215 
216  /** \brief Stores the angle step. Step is calculated with respect to number of rotations. */
217  float step_;
218 
219  /** \brief Stores the set of triangles representing the mesh. */
220  std::vector <pcl::Vertices> triangles_;
221 
222  /** \brief Stores the set of triangles for each point. Its purpose is to improve performance. */
223  std::vector <std::vector <unsigned int> > triangles_of_the_point_;
224 
225  public:
227  };
228 }
229 
230 #define PCL_INSTANTIATE_ROPSEstimation(InT, OutT) template class pcl::ROPSEstimation<InT, OutT>;
231 
232 #ifdef PCL_NO_PRECOMPILE
233 #include <pcl/features/impl/rops_estimation.hpp>
234 #endif
Feature represents the base feature class.
Definition: feature.h:107
This class implements the method for extracting RoPS features presented in the article "Rotational Pr...
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
Defines functions, macros and traits for allocating and using memory.
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
Defines all the PCL and non-PCL macros used.
#define PCL_EXPORTS
Definition: pcl_macros.h:323