Point Cloud Library (PCL)  1.11.1-dev
sac_model_cone.h
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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 
45 namespace pcl
46 {
47  /** \brief @b SampleConsensusModelCone defines a model for 3D cone segmentation.
48  * The model coefficients are defined as:
49  * <ul>
50  * <li><b>apex.x</b> : the X coordinate of cone's apex
51  * <li><b>apex.y</b> : the Y coordinate of cone's apex
52  * <li><b>apex.z</b> : the Z coordinate of cone's apex
53  * <li><b>axis_direction.x</b> : the X coordinate of the cone's axis direction
54  * <li><b>axis_direction.y</b> : the Y coordinate of the cone's axis direction
55  * <li><b>axis_direction.z</b> : the Z coordinate of the cone's axis direction
56  * <li><b>opening_angle</b> : the cone's opening angle
57  * </ul>
58  * \author Stefan Schrandt
59  * \ingroup sample_consensus
60  */
61  template <typename PointT, typename PointNT>
62  class SampleConsensusModelCone : public SampleConsensusModel<PointT>, public SampleConsensusModelFromNormals<PointT, PointNT>
63  {
64  public:
73 
77 
78  using Ptr = shared_ptr<SampleConsensusModelCone<PointT, PointNT> >;
79  using ConstPtr = shared_ptr<const SampleConsensusModelCone<PointT, PointNT>>;
80 
81  /** \brief Constructor for base SampleConsensusModelCone.
82  * \param[in] cloud the input point cloud dataset
83  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
84  */
85  SampleConsensusModelCone (const PointCloudConstPtr &cloud, bool random = false)
86  : SampleConsensusModel<PointT> (cloud, random)
88  , axis_ (Eigen::Vector3f::Zero ())
89  , eps_angle_ (0)
90  , min_angle_ (-std::numeric_limits<double>::max ())
91  , max_angle_ (std::numeric_limits<double>::max ())
92  {
93  model_name_ = "SampleConsensusModelCone";
94  sample_size_ = 3;
95  model_size_ = 7;
96  }
97 
98  /** \brief Constructor for base SampleConsensusModelCone.
99  * \param[in] cloud the input point cloud dataset
100  * \param[in] indices a vector of point indices to be used from \a cloud
101  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
102  */
104  const Indices &indices,
105  bool random = false)
106  : SampleConsensusModel<PointT> (cloud, indices, random)
108  , axis_ (Eigen::Vector3f::Zero ())
109  , eps_angle_ (0)
110  , min_angle_ (-std::numeric_limits<double>::max ())
111  , max_angle_ (std::numeric_limits<double>::max ())
112  {
113  model_name_ = "SampleConsensusModelCone";
114  sample_size_ = 3;
115  model_size_ = 7;
116  }
117 
118  /** \brief Copy constructor.
119  * \param[in] source the model to copy into this
120  */
124  eps_angle_ (), min_angle_ (), max_angle_ ()
125  {
126  *this = source;
127  model_name_ = "SampleConsensusModelCone";
128  }
129 
130  /** \brief Empty destructor */
132 
133  /** \brief Copy constructor.
134  * \param[in] source the model to copy into this
135  */
138  {
141  axis_ = source.axis_;
142  eps_angle_ = source.eps_angle_;
143  min_angle_ = source.min_angle_;
144  max_angle_ = source.max_angle_;
145  return (*this);
146  }
147 
148  /** \brief Set the angle epsilon (delta) threshold.
149  * \param[in] ea the maximum allowed difference between the cone's axis and the given axis.
150  */
151  inline void
152  setEpsAngle (double ea) { eps_angle_ = ea; }
153 
154  /** \brief Get the angle epsilon (delta) threshold. */
155  inline double
156  getEpsAngle () const { return (eps_angle_); }
157 
158  /** \brief Set the axis along which we need to search for a cone direction.
159  * \param[in] ax the axis along which we need to search for a cone direction
160  */
161  inline void
162  setAxis (const Eigen::Vector3f &ax) { axis_ = ax; }
163 
164  /** \brief Get the axis along which we need to search for a cone direction. */
165  inline Eigen::Vector3f
166  getAxis () const { return (axis_); }
167 
168  /** \brief Set the minimum and maximum allowable opening angle for a cone model
169  * given from a user.
170  * \param[in] min_angle the minimum allowable opening angle of a cone model
171  * \param[in] max_angle the maximum allowable opening angle of a cone model
172  */
173  inline void
174  setMinMaxOpeningAngle (const double &min_angle, const double &max_angle)
175  {
176  min_angle_ = min_angle;
177  max_angle_ = max_angle;
178  }
179 
180  /** \brief Get the opening angle which we need minimum to validate a cone model.
181  * \param[out] min_angle the minimum allowable opening angle of a cone model
182  * \param[out] max_angle the maximum allowable opening angle of a cone model
183  */
184  inline void
185  getMinMaxOpeningAngle (double &min_angle, double &max_angle) const
186  {
187  min_angle = min_angle_;
188  max_angle = max_angle_;
189  }
190 
191  /** \brief Check whether the given index samples can form a valid cone model, compute the model coefficients
192  * from these samples and store them in model_coefficients. The cone coefficients are: apex,
193  * axis_direction, opening_angle.
194  * \param[in] samples the point indices found as possible good candidates for creating a valid model
195  * \param[out] model_coefficients the resultant model coefficients
196  */
197  bool
198  computeModelCoefficients (const Indices &samples,
199  Eigen::VectorXf &model_coefficients) const override;
200 
201  /** \brief Compute all distances from the cloud data to a given cone model.
202  * \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
203  * \param[out] distances the resultant estimated distances
204  */
205  void
206  getDistancesToModel (const Eigen::VectorXf &model_coefficients,
207  std::vector<double> &distances) const override;
208 
209  /** \brief Select all the points which respect the given model coefficients as inliers.
210  * \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
211  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
212  * \param[out] inliers the resultant model inliers
213  */
214  void
215  selectWithinDistance (const Eigen::VectorXf &model_coefficients,
216  const double threshold,
217  Indices &inliers) override;
218 
219  /** \brief Count all the points which respect the given model coefficients as inliers.
220  *
221  * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
222  * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
223  * \return the resultant number of inliers
224  */
225  std::size_t
226  countWithinDistance (const Eigen::VectorXf &model_coefficients,
227  const double threshold) const override;
228 
229 
230  /** \brief Recompute the cone coefficients using the given inlier set and return them to the user.
231  * @note: these are the coefficients of the cone model after refinement (e.g. after SVD)
232  * \param[in] inliers the data inliers found as supporting the model
233  * \param[in] model_coefficients the initial guess for the optimization
234  * \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
235  */
236  void
237  optimizeModelCoefficients (const Indices &inliers,
238  const Eigen::VectorXf &model_coefficients,
239  Eigen::VectorXf &optimized_coefficients) const override;
240 
241 
242  /** \brief Create a new point cloud with inliers projected onto the cone model.
243  * \param[in] inliers the data inliers that we want to project on the cone model
244  * \param[in] model_coefficients the coefficients of a cone model
245  * \param[out] projected_points the resultant projected points
246  * \param[in] copy_data_fields set to true if we need to copy the other data fields
247  */
248  void
249  projectPoints (const Indices &inliers,
250  const Eigen::VectorXf &model_coefficients,
251  PointCloud &projected_points,
252  bool copy_data_fields = true) const override;
253 
254  /** \brief Verify whether a subset of indices verifies the given cone model coefficients.
255  * \param[in] indices the data indices that need to be tested against the cone model
256  * \param[in] model_coefficients the cone model coefficients
257  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
258  */
259  bool
260  doSamplesVerifyModel (const std::set<index_t> &indices,
261  const Eigen::VectorXf &model_coefficients,
262  const double threshold) const override;
263 
264  /** \brief Return a unique id for this model (SACMODEL_CONE). */
265  inline pcl::SacModel
266  getModelType () const override { return (SACMODEL_CONE); }
267 
268  protected:
271 
272  /** \brief Get the distance from a point to a line (represented by a point and a direction)
273  * \param[in] pt a point
274  * \param[in] model_coefficients the line coefficients (a point on the line, line direction)
275  */
276  double
277  pointToAxisDistance (const Eigen::Vector4f &pt, const Eigen::VectorXf &model_coefficients) const;
278 
279  /** \brief Check whether a model is valid given the user constraints.
280  * \param[in] model_coefficients the set of model coefficients
281  */
282  bool
283  isModelValid (const Eigen::VectorXf &model_coefficients) const override;
284 
285  /** \brief Check if a sample of indices results in a good sample of points
286  * indices. Pure virtual.
287  * \param[in] samples the resultant index samples
288  */
289  bool
290  isSampleGood (const Indices &samples) const override;
291 
292  private:
293  /** \brief The axis along which we need to search for a cone direction. */
294  Eigen::Vector3f axis_;
295 
296  /** \brief The maximum allowed difference between the cone direction and the given axis. */
297  double eps_angle_;
298 
299  /** \brief The minimum and maximum allowed opening angles of valid cone model. */
300  double min_angle_;
301  double max_angle_;
302 
303  /** \brief Functor for the optimization function */
304  struct OptimizationFunctor : pcl::Functor<float>
305  {
306  /** Functor constructor
307  * \param[in] indices the indices of data points to evaluate
308  * \param[in] estimator pointer to the estimator object
309  */
310  OptimizationFunctor (const pcl::SampleConsensusModelCone<PointT, PointNT> *model, const Indices& indices) :
311  pcl::Functor<float> (indices.size ()), model_ (model), indices_ (indices) {}
312 
313  /** Cost function to be minimized
314  * \param[in] x variables array
315  * \param[out] fvec resultant functions evaluations
316  * \return 0
317  */
318  int
319  operator() (const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const
320  {
321  Eigen::Vector4f apex (x[0], x[1], x[2], 0);
322  Eigen::Vector4f axis_dir (x[3], x[4], x[5], 0);
323  float opening_angle = x[6];
324 
325  float apexdotdir = apex.dot (axis_dir);
326  float dirdotdir = 1.0f / axis_dir.dot (axis_dir);
327 
328  for (int i = 0; i < values (); ++i)
329  {
330  // dist = f - r
331  Eigen::Vector4f pt = (*model_->input_)[indices_[i]].getVector4fMap();
332  pt[3] = 0;
333 
334  // Calculate the point's projection on the cone axis
335  float k = (pt.dot (axis_dir) - apexdotdir) * dirdotdir;
336  Eigen::Vector4f pt_proj = apex + k * axis_dir;
337 
338  // Calculate the actual radius of the cone at the level of the projected point
339  Eigen::Vector4f height = apex-pt_proj;
340  float actual_cone_radius = tanf (opening_angle) * height.norm ();
341 
342  fvec[i] = static_cast<float> (pcl::sqrPointToLineDistance (pt, apex, axis_dir) - actual_cone_radius * actual_cone_radius);
343  }
344  return (0);
345  }
346 
348  const Indices &indices_;
349  };
350  };
351 }
352 
353 #ifdef PCL_NO_PRECOMPILE
354 #include <pcl/sample_consensus/impl/sac_model_cone.hpp>
355 #endif
pcl::SampleConsensusModelCone::setAxis
void setAxis(const Eigen::Vector3f &ax)
Set the axis along which we need to search for a cone direction.
Definition: sac_model_cone.h:162
pcl
Definition: convolution.h:46
pcl::SampleConsensusModelCone::operator=
SampleConsensusModelCone & operator=(const SampleConsensusModelCone &source)
Copy constructor.
Definition: sac_model_cone.h:137
pcl::SampleConsensusModelCone::countWithinDistance
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.
Definition: sac_model_cone.hpp:259
pcl::SampleConsensusModelCone::isModelValid
bool isModelValid(const Eigen::VectorXf &model_coefficients) const override
Check whether a model is valid given the user constraints.
Definition: sac_model_cone.hpp:500
Eigen
Definition: bfgs.h:9
pcl::SampleConsensusModelCone::doSamplesVerifyModel
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.
Definition: sac_model_cone.hpp:447
pcl::SampleConsensusModelCone::getMinMaxOpeningAngle
void getMinMaxOpeningAngle(double &min_angle, double &max_angle) const
Get the opening angle which we need minimum to validate a cone model.
Definition: sac_model_cone.h:185
pcl::SampleConsensusModelCone::setEpsAngle
void setEpsAngle(double ea)
Set the angle epsilon (delta) threshold.
Definition: sac_model_cone.h:152
pcl::SampleConsensusModelCone::getModelType
pcl::SacModel getModelType() const override
Return a unique id for this model (SACMODEL_CONE).
Definition: sac_model_cone.h:266
pcl::SampleConsensusModelCone::~SampleConsensusModelCone
~SampleConsensusModelCone()
Empty destructor.
Definition: sac_model_cone.h:131
pcl::SampleConsensusModelFromNormals
SampleConsensusModelFromNormals represents the base model class for models that require the use of su...
Definition: sac_model.h:610
pcl::SampleConsensusModel::sample_size_
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition: sac_model.h:588
pcl::SampleConsensusModel::model_size_
unsigned int model_size_
The number of coefficients in the model.
Definition: sac_model.h:591
pcl::PointCloud< pcl::PointXYZRGB >
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:628
pcl::SampleConsensusModelCone::projectPoints
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.
Definition: sac_model_cone.hpp:351
pcl::SampleConsensusModelCone::pointToAxisDistance
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)
Definition: sac_model_cone.hpp:490
pcl::SampleConsensusModelCone::getDistancesToModel
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.
Definition: sac_model_cone.hpp:143
pcl::SampleConsensusModelCone::getEpsAngle
double getEpsAngle() const
Get the angle epsilon (delta) threshold.
Definition: sac_model_cone.h:156
pcl::Functor
Base functor all the models that need non linear optimization must define their own one and implement...
Definition: sac_model.h:672
pcl::SacModel
SacModel
Definition: model_types.h:45
pcl::SampleConsensusModel::indices_
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition: sac_model.h:556
pcl::SampleConsensusModel< pcl::PointXYZRGB >::ConstPtr
shared_ptr< const SampleConsensusModel< pcl::PointXYZRGB > > ConstPtr
Definition: sac_model.h:78
pcl::SampleConsensusModelCone::isSampleGood
bool isSampleGood(const Indices &samples) const override
Check if a sample of indices results in a good sample of points indices.
Definition: sac_model_cone.hpp:49
pcl::SampleConsensusModelCone
SampleConsensusModelCone defines a model for 3D cone segmentation.
Definition: sac_model_cone.h:62
pcl::SampleConsensusModel< pcl::PointXYZRGB >::Ptr
shared_ptr< SampleConsensusModel< pcl::PointXYZRGB > > Ptr
Definition: sac_model.h:77
pcl::SampleConsensusModel::model_name_
std::string model_name_
The model name.
Definition: sac_model.h:550
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:141
pcl::SampleConsensusModel< pcl::PointXYZRGB >::PointCloudConstPtr
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: sac_model.h:73
pcl::SampleConsensusModelCone::getAxis
Eigen::Vector3f getAxis() const
Get the axis along which we need to search for a cone direction.
Definition: sac_model_cone.h:166
pcl::SampleConsensusModelCone::SampleConsensusModelCone
SampleConsensusModelCone(const SampleConsensusModelCone &source)
Copy constructor.
Definition: sac_model_cone.h:121
distances.h
pcl::SampleConsensusModel< pcl::PointXYZRGB >::PointCloudPtr
typename PointCloud::Ptr PointCloudPtr
Definition: sac_model.h:74
pcl::SampleConsensusModelCone::SampleConsensusModelCone
SampleConsensusModelCone(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelCone.
Definition: sac_model_cone.h:85
pcl::SampleConsensusModel
SampleConsensusModel represents the base model class.
Definition: sac_model.h:69
pcl::SACMODEL_CONE
@ SACMODEL_CONE
Definition: model_types.h:53
pcl::SampleConsensusModelCone::selectWithinDistance
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.
Definition: sac_model_cone.hpp:195
pcl::SampleConsensusModelCone::computeModelCoefficients
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...
Definition: sac_model_cone.hpp:61
pcl::sqrPointToLineDistance
double sqrPointToLineDistance(const Eigen::Vector4f &pt, const Eigen::Vector4f &line_pt, const Eigen::Vector4f &line_dir)
Get the square distance from a point to a line (represented by a point and a direction)
Definition: distances.h:75
pcl::SampleConsensusModelCone::optimizeModelCoefficients
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.
Definition: sac_model_cone.hpp:313
pcl::SampleConsensusModelCone::setMinMaxOpeningAngle
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.
Definition: sac_model_cone.h:174
pcl::SampleConsensusModelCone::SampleConsensusModelCone
SampleConsensusModelCone(const PointCloudConstPtr &cloud, const Indices &indices, bool random=false)
Constructor for base SampleConsensusModelCone.
Definition: sac_model_cone.h:103