Point Cloud Library (PCL)  1.14.1-dev
sac_model_cone.h
1 /*
2  * Software License Agreement (BSD License)
3  *
4  * Point Cloud Library (PCL) - www.pointclouds.org
5  * Copyright (c) 2009-2012, Willow Garage, Inc.
6  * Copyright (c) 2012-, Open Perception, Inc.
7  *
8  * All rights reserved.
9  *
10  * Redistribution and use in source and binary forms, with or without
11  * modification, are permitted provided that the following conditions
12  * are met:
13  *
14  * * Redistributions of source code must retain the above copyright
15  * notice, this list of conditions and the following disclaimer.
16  * * Redistributions in binary form must reproduce the above
17  * copyright notice, this list of conditions and the following
18  * disclaimer in the documentation and/or other materials provided
19  * with the distribution.
20  * * Neither the name of the copyright holder(s) nor the names of its
21  * contributors may be used to endorse or promote products derived
22  * from this software without specific prior written permission.
23  *
24  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
25  * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
26  * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
27  * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
28  * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
29  * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
30  * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
31  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
32  * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
33  * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
34  * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
35  * POSSIBILITY OF SUCH DAMAGE.
36  *
37  */
38 
39 #pragma once
40 
41 #include <pcl/sample_consensus/sac_model.h>
42 #include <pcl/sample_consensus/model_types.h>
43 #include <pcl/common/distances.h>
44 #include <pcl/pcl_exports.h>
45 
46 namespace pcl
47 {
48  namespace internal {
49  PCL_EXPORTS int optimizeModelCoefficientsCone (Eigen::VectorXf& coeff, const Eigen::ArrayXf& pts_x, const Eigen::ArrayXf& pts_y, const Eigen::ArrayXf& pts_z);
50  } // namespace internal
51 
52  /** \brief @b SampleConsensusModelCone defines a model for 3D cone segmentation.
53  * The model coefficients are defined as:
54  * <ul>
55  * <li><b>apex.x</b> : the X coordinate of cone's apex
56  * <li><b>apex.y</b> : the Y coordinate of cone's apex
57  * <li><b>apex.z</b> : the Z coordinate of cone's apex
58  * <li><b>axis_direction.x</b> : the X coordinate of the cone's axis direction
59  * <li><b>axis_direction.y</b> : the Y coordinate of the cone's axis direction
60  * <li><b>axis_direction.z</b> : the Z coordinate of the cone's axis direction
61  * <li><b>opening_angle</b> : the cone's opening angle
62  * </ul>
63  * \author Stefan Schrandt
64  * \ingroup sample_consensus
65  */
66  template <typename PointT, typename PointNT>
67  class SampleConsensusModelCone : public SampleConsensusModel<PointT>, public SampleConsensusModelFromNormals<PointT, PointNT>
68  {
69  public:
78 
82 
83  using Ptr = shared_ptr<SampleConsensusModelCone<PointT, PointNT> >;
84  using ConstPtr = shared_ptr<const SampleConsensusModelCone<PointT, PointNT>>;
85 
86  /** \brief Constructor for base SampleConsensusModelCone.
87  * \param[in] cloud the input point cloud dataset
88  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
89  */
90  SampleConsensusModelCone (const PointCloudConstPtr &cloud, bool random = false)
91  : SampleConsensusModel<PointT> (cloud, random)
93  , axis_ (Eigen::Vector3f::Zero ())
94  , eps_angle_ (0)
95  , min_angle_ (-std::numeric_limits<double>::max ())
96  , max_angle_ (std::numeric_limits<double>::max ())
97  {
98  model_name_ = "SampleConsensusModelCone";
99  sample_size_ = 3;
100  model_size_ = 7;
101  }
102 
103  /** \brief Constructor for base SampleConsensusModelCone.
104  * \param[in] cloud the input point cloud dataset
105  * \param[in] indices a vector of point indices to be used from \a cloud
106  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
107  */
109  const Indices &indices,
110  bool random = false)
111  : SampleConsensusModel<PointT> (cloud, indices, random)
113  , axis_ (Eigen::Vector3f::Zero ())
114  , eps_angle_ (0)
115  , min_angle_ (-std::numeric_limits<double>::max ())
116  , max_angle_ (std::numeric_limits<double>::max ())
117  {
118  model_name_ = "SampleConsensusModelCone";
119  sample_size_ = 3;
120  model_size_ = 7;
121  }
122 
123  /** \brief Copy constructor.
124  * \param[in] source the model to copy into this
125  */
129  eps_angle_ (), min_angle_ (), max_angle_ ()
130  {
131  *this = source;
132  model_name_ = "SampleConsensusModelCone";
133  }
134 
135  /** \brief Empty destructor */
136  ~SampleConsensusModelCone () override = default;
137 
138  /** \brief Copy constructor.
139  * \param[in] source the model to copy into this
140  */
143  {
146  axis_ = source.axis_;
147  eps_angle_ = source.eps_angle_;
148  min_angle_ = source.min_angle_;
149  max_angle_ = source.max_angle_;
150  return (*this);
151  }
152 
153  /** \brief Set the angle epsilon (delta) threshold.
154  * \param[in] ea the maximum allowed difference between the cone's axis and the given axis.
155  */
156  inline void
157  setEpsAngle (double ea) { eps_angle_ = ea; }
158 
159  /** \brief Get the angle epsilon (delta) threshold. */
160  inline double
161  getEpsAngle () const { return (eps_angle_); }
162 
163  /** \brief Set the axis along which we need to search for a cone direction.
164  * \param[in] ax the axis along which we need to search for a cone direction
165  */
166  inline void
167  setAxis (const Eigen::Vector3f &ax) { axis_ = ax; }
168 
169  /** \brief Get the axis along which we need to search for a cone direction. */
170  inline Eigen::Vector3f
171  getAxis () const { return (axis_); }
172 
173  /** \brief Set the minimum and maximum allowable opening angle for a cone model
174  * given from a user.
175  * \param[in] min_angle the minimum allowable opening angle of a cone model
176  * \param[in] max_angle the maximum allowable opening angle of a cone model
177  */
178  inline void
179  setMinMaxOpeningAngle (const double &min_angle, const double &max_angle)
180  {
181  min_angle_ = min_angle;
182  max_angle_ = max_angle;
183  }
184 
185  /** \brief Get the opening angle which we need minimum to validate a cone model.
186  * \param[out] min_angle the minimum allowable opening angle of a cone model
187  * \param[out] max_angle the maximum allowable opening angle of a cone model
188  */
189  inline void
190  getMinMaxOpeningAngle (double &min_angle, double &max_angle) const
191  {
192  min_angle = min_angle_;
193  max_angle = max_angle_;
194  }
195 
196  /** \brief Check whether the given index samples can form a valid cone model, compute the model coefficients
197  * from these samples and store them in model_coefficients. The cone coefficients are: apex,
198  * axis_direction, opening_angle.
199  * \param[in] samples the point indices found as possible good candidates for creating a valid model
200  * \param[out] model_coefficients the resultant model coefficients
201  */
202  bool
203  computeModelCoefficients (const Indices &samples,
204  Eigen::VectorXf &model_coefficients) const override;
205 
206  /** \brief Compute all distances from the cloud data to a given cone model.
207  * \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
208  * \param[out] distances the resultant estimated distances
209  */
210  void
211  getDistancesToModel (const Eigen::VectorXf &model_coefficients,
212  std::vector<double> &distances) const override;
213 
214  /** \brief Select all the points which respect the given model coefficients as inliers.
215  * \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
216  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
217  * \param[out] inliers the resultant model inliers
218  */
219  void
220  selectWithinDistance (const Eigen::VectorXf &model_coefficients,
221  const double threshold,
222  Indices &inliers) override;
223 
224  /** \brief Count all the points which respect the given model coefficients as inliers.
225  *
226  * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
227  * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
228  * \return the resultant number of inliers
229  */
230  std::size_t
231  countWithinDistance (const Eigen::VectorXf &model_coefficients,
232  const double threshold) const override;
233 
234 
235  /** \brief Recompute the cone coefficients using the given inlier set and return them to the user.
236  * @note: these are the coefficients of the cone model after refinement (e.g. after SVD)
237  * \param[in] inliers the data inliers found as supporting the model
238  * \param[in] model_coefficients the initial guess for the optimization
239  * \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
240  */
241  void
242  optimizeModelCoefficients (const Indices &inliers,
243  const Eigen::VectorXf &model_coefficients,
244  Eigen::VectorXf &optimized_coefficients) const override;
245 
246 
247  /** \brief Create a new point cloud with inliers projected onto the cone model.
248  * \param[in] inliers the data inliers that we want to project on the cone model
249  * \param[in] model_coefficients the coefficients of a cone model
250  * \param[out] projected_points the resultant projected points
251  * \param[in] copy_data_fields set to true if we need to copy the other data fields
252  */
253  void
254  projectPoints (const Indices &inliers,
255  const Eigen::VectorXf &model_coefficients,
256  PointCloud &projected_points,
257  bool copy_data_fields = true) const override;
258 
259  /** \brief Verify whether a subset of indices verifies the given cone model coefficients.
260  * \param[in] indices the data indices that need to be tested against the cone model
261  * \param[in] model_coefficients the cone model coefficients
262  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
263  */
264  bool
265  doSamplesVerifyModel (const std::set<index_t> &indices,
266  const Eigen::VectorXf &model_coefficients,
267  const double threshold) const override;
268 
269  /** \brief Return a unique id for this model (SACMODEL_CONE). */
270  inline pcl::SacModel
271  getModelType () const override { return (SACMODEL_CONE); }
272 
273  protected:
276 
277  /** \brief Get the distance from a point to a line (represented by a point and a direction)
278  * \param[in] pt a point
279  * \param[in] model_coefficients the line coefficients (a point on the line, line direction)
280  */
281  double
282  pointToAxisDistance (const Eigen::Vector4f &pt, const Eigen::VectorXf &model_coefficients) const;
283 
284  /** \brief Check whether a model is valid given the user constraints.
285  * \param[in] model_coefficients the set of model coefficients
286  */
287  bool
288  isModelValid (const Eigen::VectorXf &model_coefficients) const override;
289 
290  /** \brief Check if a sample of indices results in a good sample of points
291  * indices. Pure virtual.
292  * \param[in] samples the resultant index samples
293  */
294  bool
295  isSampleGood (const Indices &samples) const override;
296 
297  private:
298  /** \brief The axis along which we need to search for a cone direction. */
299  Eigen::Vector3f axis_;
300 
301  /** \brief The maximum allowed difference between the cone direction and the given axis. */
302  double eps_angle_;
303 
304  /** \brief The minimum and maximum allowed opening angles of valid cone model. */
305  double min_angle_;
306  double max_angle_;
307  };
308 }
309 
310 #ifdef PCL_NO_PRECOMPILE
311 #include <pcl/sample_consensus/impl/sac_model_cone.hpp>
312 #endif
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
SampleConsensusModelCone defines a model for 3D cone segmentation.
void optimizeModelCoefficients(const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const override
Recompute the cone coefficients using the given inlier set and return them to the user.
void setAxis(const Eigen::Vector3f &ax)
Set the axis along which we need to search for a cone direction.
SampleConsensusModelCone(const PointCloudConstPtr &cloud, const Indices &indices, bool random=false)
Constructor for base SampleConsensusModelCone.
SampleConsensusModelCone(const SampleConsensusModelCone &source)
Copy constructor.
~SampleConsensusModelCone() override=default
Empty destructor.
void projectPoints(const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true) const override
Create a new point cloud with inliers projected onto the cone model.
pcl::SacModel getModelType() const override
Return a unique id for this model (SACMODEL_CONE).
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all distances from the cloud data to a given cone model.
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers) override
Select all the points which respect the given model coefficients as inliers.
bool isSampleGood(const Indices &samples) const override
Check if a sample of indices results in a good sample of points indices.
bool computeModelCoefficients(const Indices &samples, Eigen::VectorXf &model_coefficients) const override
Check whether the given index samples can form a valid cone model, compute the model coefficients fro...
bool isModelValid(const Eigen::VectorXf &model_coefficients) const override
Check whether a model is valid given the user constraints.
Eigen::Vector3f getAxis() const
Get the axis along which we need to search for a cone direction.
double pointToAxisDistance(const Eigen::Vector4f &pt, const Eigen::VectorXf &model_coefficients) const
Get the distance from a point to a line (represented by a point and a direction)
void setEpsAngle(double ea)
Set the angle epsilon (delta) threshold.
void getMinMaxOpeningAngle(double &min_angle, double &max_angle) const
Get the opening angle which we need minimum to validate a cone model.
double getEpsAngle() const
Get the angle epsilon (delta) threshold.
bool doSamplesVerifyModel(const std::set< index_t > &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const override
Verify whether a subset of indices verifies the given cone model coefficients.
SampleConsensusModelCone & operator=(const SampleConsensusModelCone &source)
Copy constructor.
void setMinMaxOpeningAngle(const double &min_angle, const double &max_angle)
Set the minimum and maximum allowable opening angle for a cone model given from a user.
SampleConsensusModelCone(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelCone.
std::size_t countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold) const override
Count all the points which respect the given model coefficients as inliers.
SampleConsensusModelFromNormals represents the base model class for models that require the use of su...
Definition: sac_model.h:613
SampleConsensusModel represents the base model class.
Definition: sac_model.h:71
shared_ptr< SampleConsensusModel< PointT > > Ptr
Definition: sac_model.h:78
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition: sac_model.h:589
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: sac_model.h:74
std::string model_name_
The model name.
Definition: sac_model.h:551
unsigned int model_size_
The number of coefficients in the model.
Definition: sac_model.h:592
typename PointCloud::Ptr PointCloudPtr
Definition: sac_model.h:75
shared_ptr< const SampleConsensusModel< PointT > > ConstPtr
Definition: sac_model.h:79
Define standard C methods to do distance calculations.
Definition: bfgs.h:10
PCL_EXPORTS int optimizeModelCoefficientsCone(Eigen::VectorXf &coeff, const Eigen::ArrayXf &pts_x, const Eigen::ArrayXf &pts_y, const Eigen::ArrayXf &pts_z)
SacModel
Definition: model_types.h:46
@ SACMODEL_CONE
Definition: model_types.h:53
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
#define PCL_EXPORTS
Definition: pcl_macros.h:323
A point structure representing Euclidean xyz coordinates, and the RGB color.