Point Cloud Library (PCL)  1.9.1
sac_model_cone.h
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38 
39 #ifndef PCL_SAMPLE_CONSENSUS_MODEL_CONE_H_
40 #define PCL_SAMPLE_CONSENSUS_MODEL_CONE_H_
41 
42 #include <pcl/sample_consensus/sac_model.h>
43 #include <pcl/sample_consensus/model_types.h>
44 #include <pcl/common/common.h>
45 #include <pcl/common/distances.h>
46 #include <limits.h>
47 
48 namespace pcl
49 {
50  /** \brief @b SampleConsensusModelCone defines a model for 3D cone segmentation.
51  * The model coefficients are defined as:
52  * <ul>
53  * <li><b>apex.x</b> : the X coordinate of cone's apex
54  * <li><b>apex.y</b> : the Y coordinate of cone's apex
55  * <li><b>apex.z</b> : the Z coordinate of cone's apex
56  * <li><b>axis_direction.x</b> : the X coordinate of the cone's axis direction
57  * <li><b>axis_direction.y</b> : the Y coordinate of the cone's axis direction
58  * <li><b>axis_direction.z</b> : the Z coordinate of the cone's axis direction
59  * <li><b>opening_angle</b> : the cone's opening angle
60  * </ul>
61  * \author Stefan Schrandt
62  * \ingroup sample_consensus
63  */
64  template <typename PointT, typename PointNT>
65  class SampleConsensusModelCone : public SampleConsensusModel<PointT>, public SampleConsensusModelFromNormals<PointT, PointNT>
66  {
67  public:
76 
80 
81  typedef boost::shared_ptr<SampleConsensusModelCone> Ptr;
82 
83  /** \brief Constructor for base SampleConsensusModelCone.
84  * \param[in] cloud the input point cloud dataset
85  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
86  */
87  SampleConsensusModelCone (const PointCloudConstPtr &cloud, bool random = false)
88  : SampleConsensusModel<PointT> (cloud, random)
90  , axis_ (Eigen::Vector3f::Zero ())
91  , eps_angle_ (0)
92  , min_angle_ (-std::numeric_limits<double>::max ())
93  , max_angle_ (std::numeric_limits<double>::max ())
94  {
95  model_name_ = "SampleConsensusModelCone";
96  sample_size_ = 3;
97  model_size_ = 7;
98  }
99 
100  /** \brief Constructor for base SampleConsensusModelCone.
101  * \param[in] cloud the input point cloud dataset
102  * \param[in] indices a vector of point indices to be used from \a cloud
103  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
104  */
106  const std::vector<int> &indices,
107  bool random = false)
108  : SampleConsensusModel<PointT> (cloud, indices, random)
110  , axis_ (Eigen::Vector3f::Zero ())
111  , eps_angle_ (0)
112  , min_angle_ (-std::numeric_limits<double>::max ())
113  , max_angle_ (std::numeric_limits<double>::max ())
114  {
115  model_name_ = "SampleConsensusModelCone";
116  sample_size_ = 3;
117  model_size_ = 7;
118  }
119 
120  /** \brief Copy constructor.
121  * \param[in] source the model to copy into this
122  */
126  axis_ (), eps_angle_ (), min_angle_ (), max_angle_ ()
127  {
128  *this = source;
129  model_name_ = "SampleConsensusModelCone";
130  }
131 
132  /** \brief Empty destructor */
134 
135  /** \brief Copy constructor.
136  * \param[in] source the model to copy into this
137  */
140  {
143  axis_ = source.axis_;
144  eps_angle_ = source.eps_angle_;
145  min_angle_ = source.min_angle_;
146  max_angle_ = source.max_angle_;
147  return (*this);
148  }
149 
150  /** \brief Set the angle epsilon (delta) threshold.
151  * \param[in] ea the maximum allowed difference between the cone's axis and the given axis.
152  */
153  inline void
154  setEpsAngle (double ea) { eps_angle_ = ea; }
155 
156  /** \brief Get the angle epsilon (delta) threshold. */
157  inline double
158  getEpsAngle () const { return (eps_angle_); }
159 
160  /** \brief Set the axis along which we need to search for a cone direction.
161  * \param[in] ax the axis along which we need to search for a cone direction
162  */
163  inline void
164  setAxis (const Eigen::Vector3f &ax) { axis_ = ax; }
165 
166  /** \brief Get the axis along which we need to search for a cone direction. */
167  inline Eigen::Vector3f
168  getAxis () const { return (axis_); }
169 
170  /** \brief Set the minimum and maximum allowable opening angle for a cone model
171  * given from a user.
172  * \param[in] min_angle the minimum allowable opening angle of a cone model
173  * \param[in] max_angle the maximum allowable opening angle of a cone model
174  */
175  inline void
176  setMinMaxOpeningAngle (const double &min_angle, const double &max_angle)
177  {
178  min_angle_ = min_angle;
179  max_angle_ = max_angle;
180  }
181 
182  /** \brief Get the opening angle which we need minimum to validate a cone model.
183  * \param[out] min_angle the minimum allowable opening angle of a cone model
184  * \param[out] max_angle the maximum allowable opening angle of a cone model
185  */
186  inline void
187  getMinMaxOpeningAngle (double &min_angle, double &max_angle) const
188  {
189  min_angle = min_angle_;
190  max_angle = max_angle_;
191  }
192 
193  /** \brief Check whether the given index samples can form a valid cone model, compute the model coefficients
194  * from these samples and store them in model_coefficients. The cone coefficients are: apex,
195  * axis_direction, opening_angle.
196  * \param[in] samples the point indices found as possible good candidates for creating a valid model
197  * \param[out] model_coefficients the resultant model coefficients
198  */
199  bool
200  computeModelCoefficients (const std::vector<int> &samples,
201  Eigen::VectorXf &model_coefficients) const;
202 
203  /** \brief Compute all distances from the cloud data to a given cone model.
204  * \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
205  * \param[out] distances the resultant estimated distances
206  */
207  void
208  getDistancesToModel (const Eigen::VectorXf &model_coefficients,
209  std::vector<double> &distances) const;
210 
211  /** \brief Select all the points which respect the given model coefficients as inliers.
212  * \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
213  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
214  * \param[out] inliers the resultant model inliers
215  */
216  void
217  selectWithinDistance (const Eigen::VectorXf &model_coefficients,
218  const double threshold,
219  std::vector<int> &inliers);
220 
221  /** \brief Count all the points which respect the given model coefficients as inliers.
222  *
223  * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
224  * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
225  * \return the resultant number of inliers
226  */
227  virtual int
228  countWithinDistance (const Eigen::VectorXf &model_coefficients,
229  const double threshold) const;
230 
231 
232  /** \brief Recompute the cone coefficients using the given inlier set and return them to the user.
233  * @note: these are the coefficients of the cone model after refinement (e.g. after SVD)
234  * \param[in] inliers the data inliers found as supporting the model
235  * \param[in] model_coefficients the initial guess for the optimization
236  * \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
237  */
238  void
239  optimizeModelCoefficients (const std::vector<int> &inliers,
240  const Eigen::VectorXf &model_coefficients,
241  Eigen::VectorXf &optimized_coefficients) const;
242 
243 
244  /** \brief Create a new point cloud with inliers projected onto the cone model.
245  * \param[in] inliers the data inliers that we want to project on the cone model
246  * \param[in] model_coefficients the coefficients of a cone model
247  * \param[out] projected_points the resultant projected points
248  * \param[in] copy_data_fields set to true if we need to copy the other data fields
249  */
250  void
251  projectPoints (const std::vector<int> &inliers,
252  const Eigen::VectorXf &model_coefficients,
253  PointCloud &projected_points,
254  bool copy_data_fields = true) const;
255 
256  /** \brief Verify whether a subset of indices verifies the given cone model coefficients.
257  * \param[in] indices the data indices that need to be tested against the cone model
258  * \param[in] model_coefficients the cone model coefficients
259  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
260  */
261  bool
262  doSamplesVerifyModel (const std::set<int> &indices,
263  const Eigen::VectorXf &model_coefficients,
264  const double threshold) const;
265 
266  /** \brief Return an unique id for this model (SACMODEL_CONE). */
267  inline pcl::SacModel
268  getModelType () const { return (SACMODEL_CONE); }
269 
270  protected:
273 
274  /** \brief Get the distance from a point to a line (represented by a point and a direction)
275  * \param[in] pt a point
276  * \param[in] model_coefficients the line coefficients (a point on the line, line direction)
277  */
278  double
279  pointToAxisDistance (const Eigen::Vector4f &pt, const Eigen::VectorXf &model_coefficients) const;
280 
281  /** \brief Check whether a model is valid given the user constraints.
282  * \param[in] model_coefficients the set of model coefficients
283  */
284  virtual bool
285  isModelValid (const Eigen::VectorXf &model_coefficients) const;
286 
287  /** \brief Check if a sample of indices results in a good sample of points
288  * indices. Pure virtual.
289  * \param[in] samples the resultant index samples
290  */
291  bool
292  isSampleGood (const std::vector<int> &samples) const;
293 
294  private:
295  /** \brief The axis along which we need to search for a plane perpendicular to. */
296  Eigen::Vector3f axis_;
297 
298  /** \brief The maximum allowed difference between the plane normal and the given axis. */
299  double eps_angle_;
300 
301  /** \brief The minimum and maximum allowed opening angles of valid cone model. */
302  double min_angle_;
303  double max_angle_;
304 
305 #if defined BUILD_Maintainer && defined __GNUC__ && __GNUC__ == 4 && __GNUC_MINOR__ > 3
306 #pragma GCC diagnostic ignored "-Weffc++"
307 #endif
308  /** \brief Functor for the optimization function */
309  struct OptimizationFunctor : pcl::Functor<float>
310  {
311  /** Functor constructor
312  * \param[in] indices the indices of data points to evaluate
313  * \param[in] estimator pointer to the estimator object
314  */
315  OptimizationFunctor (const pcl::SampleConsensusModelCone<PointT, PointNT> *model, const std::vector<int>& indices) :
316  pcl::Functor<float> (indices.size ()), model_ (model), indices_ (indices) {}
317 
318  /** Cost function to be minimized
319  * \param[in] x variables array
320  * \param[out] fvec resultant functions evaluations
321  * \return 0
322  */
323  int
324  operator() (const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const
325  {
326  Eigen::Vector4f apex (x[0], x[1], x[2], 0);
327  Eigen::Vector4f axis_dir (x[3], x[4], x[5], 0);
328  float opening_angle = x[6];
329 
330  float apexdotdir = apex.dot (axis_dir);
331  float dirdotdir = 1.0f / axis_dir.dot (axis_dir);
332 
333  for (int i = 0; i < values (); ++i)
334  {
335  // dist = f - r
336  Eigen::Vector4f pt (model_->input_->points[indices_[i]].x,
337  model_->input_->points[indices_[i]].y,
338  model_->input_->points[indices_[i]].z, 0);
339 
340  // Calculate the point's projection on the cone axis
341  float k = (pt.dot (axis_dir) - apexdotdir) * dirdotdir;
342  Eigen::Vector4f pt_proj = apex + k * axis_dir;
343 
344  // Calculate the actual radius of the cone at the level of the projected point
345  Eigen::Vector4f height = apex-pt_proj;
346  float actual_cone_radius = tanf (opening_angle) * height.norm ();
347 
348  fvec[i] = static_cast<float> (pcl::sqrPointToLineDistance (pt, apex, axis_dir) - actual_cone_radius * actual_cone_radius);
349  }
350  return (0);
351  }
352 
354  const std::vector<int> &indices_;
355  };
356 #if defined BUILD_Maintainer && defined __GNUC__ && __GNUC__ == 4 && __GNUC_MINOR__ > 3
357 #pragma GCC diagnostic warning "-Weffc++"
358 #endif
359  };
360 }
361 
362 #ifdef PCL_NO_PRECOMPILE
363 #include <pcl/sample_consensus/impl/sac_model_cone.hpp>
364 #endif
365 
366 #endif //#ifndef PCL_SAMPLE_CONSENSUS_MODEL_CONE_H_
pcl::SampleConsensusModelCone::projectPoints
void projectPoints(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true) const
Create a new point cloud with inliers projected onto the cone model.
Definition: sac_model_cone.hpp:346
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:164
pcl::SampleConsensusModel::indices_
boost::shared_ptr< std::vector< int > > indices_
A pointer to the vector of point indices to use.
Definition: sac_model.h:540
pcl
This file defines compatibility wrappers for low level I/O functions.
Definition: convolution.h:45
pcl::SampleConsensusModelCone::operator=
SampleConsensusModelCone & operator=(const SampleConsensusModelCone &source)
Copy constructor.
Definition: sac_model_cone.h:139
Eigen
Definition: bfgs.h:10
common.h
pcl::SampleConsensusModelCone::SampleConsensusModelCone
SampleConsensusModelCone(const PointCloudConstPtr &cloud, const std::vector< int > &indices, bool random=false)
Constructor for base SampleConsensusModelCone.
Definition: sac_model_cone.h:105
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:187
pcl::SampleConsensusModel::PointCloudPtr
pcl::PointCloud< PointT >::Ptr PointCloudPtr
Definition: sac_model.h:71
pcl::SampleConsensusModelCone::Ptr
boost::shared_ptr< SampleConsensusModelCone > Ptr
Definition: sac_model_cone.h:81
pcl::SampleConsensusModelCone::setEpsAngle
void setEpsAngle(double ea)
Set the angle epsilon (delta) threshold.
Definition: sac_model_cone.h:154
pcl::SampleConsensusModelFromNormals
SampleConsensusModelFromNormals represents the base model class for models that require the use of su...
Definition: sac_model.h:591
pcl::SampleConsensusModelCone::optimizeModelCoefficients
void optimizeModelCoefficients(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
Recompute the cone coefficients using the given inlier set and return them to the user.
Definition: sac_model_cone.hpp:309
pcl::SampleConsensusModelCone::PointCloudConstPtr
SampleConsensusModel< PointT >::PointCloudConstPtr PointCloudConstPtr
Definition: sac_model_cone.h:79
pcl::SampleConsensusModelCone::isSampleGood
bool isSampleGood(const std::vector< int > &samples) const
Check if a sample of indices results in a good sample of points indices.
Definition: sac_model_cone.hpp:48
pcl::SampleConsensusModel::sample_size_
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition: sac_model.h:572
pcl::SampleConsensusModel::model_size_
unsigned int model_size_
The number of coefficients in the model.
Definition: sac_model.h:575
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: projection_matrix.h:53
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:619
pcl::SampleConsensusModelCone::isModelValid
virtual bool isModelValid(const Eigen::VectorXf &model_coefficients) const
Check whether a model is valid given the user constraints.
Definition: sac_model_cone.hpp:495
pcl::SampleConsensusModel::PointCloudConstPtr
pcl::PointCloud< PointT >::ConstPtr PointCloudConstPtr
Definition: sac_model.h:70
pcl::SampleConsensusModelCone::doSamplesVerifyModel
bool doSamplesVerifyModel(const std::set< int > &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
Verify whether a subset of indices verifies the given cone model coefficients.
Definition: sac_model_cone.hpp:442
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:485
pcl::SampleConsensusModelCone::getDistancesToModel
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const
Compute all distances from the cloud data to a given cone model.
Definition: sac_model_cone.hpp:137
pcl::SampleConsensusModelCone::getEpsAngle
double getEpsAngle() const
Get the angle epsilon (delta) threshold.
Definition: sac_model_cone.h:158
pcl::Functor
Base functor all the models that need non linear optimization must define their own one and implement...
Definition: sac_model.h:653
pcl::SacModel
SacModel
Definition: model_types.h:46
pcl::SampleConsensusModelCone::selectWithinDistance
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, std::vector< int > &inliers)
Select all the points which respect the given model coefficients as inliers.
Definition: sac_model_cone.hpp:189
pcl::SampleConsensusModelCone
SampleConsensusModelCone defines a model for 3D cone segmentation.
Definition: sac_model_cone.h:65
pcl::SampleConsensusModel::model_name_
std::string model_name_
The model name.
Definition: sac_model.h:534
pcl::SampleConsensusModelCone::PointCloud
SampleConsensusModel< PointT >::PointCloud PointCloud
Definition: sac_model_cone.h:77
pcl::SampleConsensusModelCone::getModelType
pcl::SacModel getModelType() const
Return an unique id for this model (SACMODEL_CONE).
Definition: sac_model_cone.h:268
pcl::SampleConsensusModelCone::countWithinDistance
virtual int countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold) const
Count all the points which respect the given model coefficients as inliers.
Definition: sac_model_cone.hpp:255
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:168
pcl::SampleConsensusModelCone::SampleConsensusModelCone
SampleConsensusModelCone(const SampleConsensusModelCone &source)
Copy constructor.
Definition: sac_model_cone.h:123
std
Definition: multi_grid_octree_data.hpp:45
distances.h
pcl::SampleConsensusModelCone::SampleConsensusModelCone
SampleConsensusModelCone(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelCone.
Definition: sac_model_cone.h:87
pcl::SampleConsensusModel
SampleConsensusModel represents the base model class.
Definition: sac_model.h:66
pcl::SACMODEL_CONE
@ SACMODEL_CONE
Definition: model_types.h:54
pcl::SampleConsensusModelCone::PointCloudPtr
SampleConsensusModel< PointT >::PointCloudPtr PointCloudPtr
Definition: sac_model_cone.h:78
pcl::SampleConsensusModelCone::~SampleConsensusModelCone
virtual ~SampleConsensusModelCone()
Empty destructor.
Definition: sac_model_cone.h:133
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:69
pcl::SampleConsensusModelCone::computeModelCoefficients
bool computeModelCoefficients(const std::vector< int > &samples, Eigen::VectorXf &model_coefficients) const
Check whether the given index samples can form a valid cone model, compute the model coefficients fro...
Definition: sac_model_cone.hpp:55
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:176