Point Cloud Library (PCL)  1.8.1
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/pcl_macros.h>
45 #include <pcl/common/common.h>
46 #include <pcl/common/distances.h>
47 #include <limits.h>
48 
49 namespace pcl
50 {
51  /** \brief @b SampleConsensusModelCone defines a model for 3D cone segmentation.
52  * The model coefficients are defined as:
53  * <ul>
54  * <li><b>apex.x</b> : the X coordinate of cone's apex
55  * <li><b>apex.y</b> : the Y coordinate of cone's apex
56  * <li><b>apex.z</b> : the Z coordinate of cone's apex
57  * <li><b>axis_direction.x</b> : the X coordinate of the cone's axis direction
58  * <li><b>axis_direction.y</b> : the Y coordinate of the cone's axis direction
59  * <li><b>axis_direction.z</b> : the Z coordinate of the cone's axis direction
60  * <li><b>opening_angle</b> : the cone's opening angle
61  * </ul>
62  * \author Stefan Schrandt
63  * \ingroup sample_consensus
64  */
65  template <typename PointT, typename PointNT>
66  class SampleConsensusModelCone : public SampleConsensusModel<PointT>, public SampleConsensusModelFromNormals<PointT, PointNT>
67  {
68  public:
77 
81 
82  typedef boost::shared_ptr<SampleConsensusModelCone> Ptr;
83 
84  /** \brief Constructor for base SampleConsensusModelCone.
85  * \param[in] cloud the input point cloud dataset
86  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
87  */
88  SampleConsensusModelCone (const PointCloudConstPtr &cloud, bool random = false)
89  : SampleConsensusModel<PointT> (cloud, random)
91  , axis_ (Eigen::Vector3f::Zero ())
92  , eps_angle_ (0)
93  , min_angle_ (-std::numeric_limits<double>::max ())
94  , max_angle_ (std::numeric_limits<double>::max ())
95  , tmp_inliers_ ()
96  {
97  model_name_ = "SampleConsensusModelCone";
98  sample_size_ = 3;
99  model_size_ = 7;
100  }
101 
102  /** \brief Constructor for base SampleConsensusModelCone.
103  * \param[in] cloud the input point cloud dataset
104  * \param[in] indices a vector of point indices to be used from \a cloud
105  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
106  */
107  SampleConsensusModelCone (const PointCloudConstPtr &cloud,
108  const std::vector<int> &indices,
109  bool random = false)
110  : SampleConsensusModel<PointT> (cloud, indices, random)
112  , axis_ (Eigen::Vector3f::Zero ())
113  , eps_angle_ (0)
114  , min_angle_ (-std::numeric_limits<double>::max ())
115  , max_angle_ (std::numeric_limits<double>::max ())
116  , tmp_inliers_ ()
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  axis_ (), eps_angle_ (), min_angle_ (), max_angle_ (), tmp_inliers_ ()
130  {
131  *this = source;
132  model_name_ = "SampleConsensusModelCone";
133  }
134 
135  /** \brief Empty destructor */
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  tmp_inliers_ = source.tmp_inliers_;
151  return (*this);
152  }
153 
154  /** \brief Set the angle epsilon (delta) threshold.
155  * \param[in] ea the maximum allowed difference between the cone's axis and the given axis.
156  */
157  inline void
158  setEpsAngle (double ea) { eps_angle_ = ea; }
159 
160  /** \brief Get the angle epsilon (delta) threshold. */
161  inline double
162  getEpsAngle () const { return (eps_angle_); }
163 
164  /** \brief Set the axis along which we need to search for a cone direction.
165  * \param[in] ax the axis along which we need to search for a cone direction
166  */
167  inline void
168  setAxis (const Eigen::Vector3f &ax) { axis_ = ax; }
169 
170  /** \brief Get the axis along which we need to search for a cone direction. */
171  inline Eigen::Vector3f
172  getAxis () const { return (axis_); }
173 
174  /** \brief Set the minimum and maximum allowable opening angle for a cone model
175  * given from a user.
176  * \param[in] min_angle the minimum allowable opening angle of a cone model
177  * \param[in] max_angle the maximum allowable opening angle of a cone model
178  */
179  inline void
180  setMinMaxOpeningAngle (const double &min_angle, const double &max_angle)
181  {
182  min_angle_ = min_angle;
183  max_angle_ = max_angle;
184  }
185 
186  /** \brief Get the opening angle which we need minimum to validate a cone model.
187  * \param[out] min_angle the minimum allowable opening angle of a cone model
188  * \param[out] max_angle the maximum allowable opening angle of a cone model
189  */
190  inline void
191  getMinMaxOpeningAngle (double &min_angle, double &max_angle) const
192  {
193  min_angle = min_angle_;
194  max_angle = max_angle_;
195  }
196 
197  /** \brief Check whether the given index samples can form a valid cone model, compute the model coefficients
198  * from these samples and store them in model_coefficients. The cone coefficients are: apex,
199  * axis_direction, opening_angle.
200  * \param[in] samples the point indices found as possible good candidates for creating a valid model
201  * \param[out] model_coefficients the resultant model coefficients
202  */
203  bool
204  computeModelCoefficients (const std::vector<int> &samples,
205  Eigen::VectorXf &model_coefficients);
206 
207  /** \brief Compute all distances from the cloud data to a given cone model.
208  * \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
209  * \param[out] distances the resultant estimated distances
210  */
211  void
212  getDistancesToModel (const Eigen::VectorXf &model_coefficients,
213  std::vector<double> &distances);
214 
215  /** \brief Select all the points which respect the given model coefficients as inliers.
216  * \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
217  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
218  * \param[out] inliers the resultant model inliers
219  */
220  void
221  selectWithinDistance (const Eigen::VectorXf &model_coefficients,
222  const double threshold,
223  std::vector<int> &inliers);
224 
225  /** \brief Count all the points which respect the given model coefficients as inliers.
226  *
227  * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
228  * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
229  * \return the resultant number of inliers
230  */
231  virtual int
232  countWithinDistance (const Eigen::VectorXf &model_coefficients,
233  const double threshold);
234 
235 
236  /** \brief Recompute the cone coefficients using the given inlier set and return them to the user.
237  * @note: these are the coefficients of the cone model after refinement (e.g. after SVD)
238  * \param[in] inliers the data inliers found as supporting the model
239  * \param[in] model_coefficients the initial guess for the optimization
240  * \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
241  */
242  void
243  optimizeModelCoefficients (const std::vector<int> &inliers,
244  const Eigen::VectorXf &model_coefficients,
245  Eigen::VectorXf &optimized_coefficients);
246 
247 
248  /** \brief Create a new point cloud with inliers projected onto the cone model.
249  * \param[in] inliers the data inliers that we want to project on the cone model
250  * \param[in] model_coefficients the coefficients of a cone model
251  * \param[out] projected_points the resultant projected points
252  * \param[in] copy_data_fields set to true if we need to copy the other data fields
253  */
254  void
255  projectPoints (const std::vector<int> &inliers,
256  const Eigen::VectorXf &model_coefficients,
257  PointCloud &projected_points,
258  bool copy_data_fields = true);
259 
260  /** \brief Verify whether a subset of indices verifies the given cone model coefficients.
261  * \param[in] indices the data indices that need to be tested against the cone model
262  * \param[in] model_coefficients the cone model coefficients
263  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
264  */
265  bool
266  doSamplesVerifyModel (const std::set<int> &indices,
267  const Eigen::VectorXf &model_coefficients,
268  const double threshold);
269 
270  /** \brief Return an unique id for this model (SACMODEL_CONE). */
271  inline pcl::SacModel
272  getModelType () const { return (SACMODEL_CONE); }
273 
274  protected:
277 
278  /** \brief Get the distance from a point to a line (represented by a point and a direction)
279  * \param[in] pt a point
280  * \param[in] model_coefficients the line coefficients (a point on the line, line direction)
281  */
282  double
283  pointToAxisDistance (const Eigen::Vector4f &pt, const Eigen::VectorXf &model_coefficients);
284 
285  /** \brief Get a string representation of the name of this class. */
286  PCL_DEPRECATED ("[pcl::SampleConsensusModelCone::getName] getName is deprecated. Please use getClassName instead.")
287  std::string
288  getName () const { return (model_name_); }
289 
290  protected:
291  /** \brief Check whether a model is valid given the user constraints.
292  * \param[in] model_coefficients the set of model coefficients
293  */
294  virtual bool
295  isModelValid (const Eigen::VectorXf &model_coefficients);
296 
297  /** \brief Check if a sample of indices results in a good sample of points
298  * indices. Pure virtual.
299  * \param[in] samples the resultant index samples
300  */
301  bool
302  isSampleGood (const std::vector<int> &samples) const;
303 
304  private:
305  /** \brief The axis along which we need to search for a plane perpendicular to. */
306  Eigen::Vector3f axis_;
307 
308  /** \brief The maximum allowed difference between the plane normal and the given axis. */
309  double eps_angle_;
310 
311  /** \brief The minimum and maximum allowed opening angles of valid cone model. */
312  double min_angle_;
313  double max_angle_;
314 
315  /** \brief temporary pointer to a list of given indices for optimizeModelCoefficients () */
316  const std::vector<int> *tmp_inliers_;
317 
318 #if defined BUILD_Maintainer && defined __GNUC__ && __GNUC__ == 4 && __GNUC_MINOR__ > 3
319 #pragma GCC diagnostic ignored "-Weffc++"
320 #endif
321  /** \brief Functor for the optimization function */
322  struct OptimizationFunctor : pcl::Functor<float>
323  {
324  /** Functor constructor
325  * \param[in] m_data_points the number of data points to evaluate
326  * \param[in] estimator pointer to the estimator object
327  * \param[in] distance distance computation function pointer
328  */
329  OptimizationFunctor (int m_data_points, pcl::SampleConsensusModelCone<PointT, PointNT> *model) :
330  pcl::Functor<float> (m_data_points), model_ (model) {}
331 
332  /** Cost function to be minimized
333  * \param[in] x variables array
334  * \param[out] fvec resultant functions evaluations
335  * \return 0
336  */
337  int
338  operator() (const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const
339  {
340  Eigen::Vector4f apex (x[0], x[1], x[2], 0);
341  Eigen::Vector4f axis_dir (x[3], x[4], x[5], 0);
342  float opening_angle = x[6];
343 
344  float apexdotdir = apex.dot (axis_dir);
345  float dirdotdir = 1.0f / axis_dir.dot (axis_dir);
346 
347  for (int i = 0; i < values (); ++i)
348  {
349  // dist = f - r
350  Eigen::Vector4f pt (model_->input_->points[(*model_->tmp_inliers_)[i]].x,
351  model_->input_->points[(*model_->tmp_inliers_)[i]].y,
352  model_->input_->points[(*model_->tmp_inliers_)[i]].z, 0);
353 
354  // Calculate the point's projection on the cone axis
355  float k = (pt.dot (axis_dir) - apexdotdir) * dirdotdir;
356  Eigen::Vector4f pt_proj = apex + k * axis_dir;
357 
358  // Calculate the actual radius of the cone at the level of the projected point
359  Eigen::Vector4f height = apex-pt_proj;
360  float actual_cone_radius = tanf (opening_angle) * height.norm ();
361 
362  fvec[i] = static_cast<float> (pcl::sqrPointToLineDistance (pt, apex, axis_dir) - actual_cone_radius * actual_cone_radius);
363  }
364  return (0);
365  }
366 
368  };
369 #if defined BUILD_Maintainer && defined __GNUC__ && __GNUC__ == 4 && __GNUC_MINOR__ > 3
370 #pragma GCC diagnostic warning "-Weffc++"
371 #endif
372  };
373 }
374 
375 #ifdef PCL_NO_PRECOMPILE
376 #include <pcl/sample_consensus/impl/sac_model_cone.hpp>
377 #endif
378 
379 #endif //#ifndef PCL_SAMPLE_CONSENSUS_MODEL_CONE_H_
bool computeModelCoefficients(const std::vector< int > &samples, Eigen::VectorXf &model_coefficients)
Check whether the given index samples can form a valid cone model, compute the model coefficients fro...
SampleConsensusModel< PointT >::PointCloudConstPtr PointCloudConstPtr
void getMinMaxOpeningAngle(double &min_angle, double &max_angle) const
Get the opening angle which we need minimum to validate a cone model.
virtual ~SampleConsensusModelCone()
Empty destructor.
bool isSampleGood(const std::vector< int > &samples) const
Check if a sample of indices results in a good sample of points indices.
unsigned int model_size_
The number of coefficients in the model.
Definition: sac_model.h:575
void setEpsAngle(double ea)
Set the angle epsilon (delta) threshold.
Base functor all the models that need non linear optimization must define their own one and implement...
Definition: sac_model.h:653
double pointToAxisDistance(const Eigen::Vector4f &pt, const Eigen::VectorXf &model_coefficients)
Get the distance from a point to a line (represented by a point and a direction)
void optimizeModelCoefficients(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients)
Recompute the cone coefficients using the given inlier set and return them to the user...
Definition: bfgs.h:10
Define standard C methods to do distance calculations.
bool doSamplesVerifyModel(const std::set< int > &indices, const Eigen::VectorXf &model_coefficients, const double threshold)
Verify whether a subset of indices verifies the given cone model coefficients.
Define standard C methods and C++ classes that are common to all methods.
SampleConsensusModel represents the base model class.
Definition: sac_model.h:66
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
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.
std::string model_name_
The model name.
Definition: sac_model.h:534
pcl::PointCloud< PointT >::Ptr PointCloudPtr
Definition: sac_model.h:71
double getEpsAngle() const
Get the angle epsilon (delta) threshold.
SampleConsensusModel< PointT >::PointCloud PointCloud
virtual bool isModelValid(const Eigen::VectorXf &model_coefficients)
Check whether a model is valid given the user constraints.
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances)
Compute all distances from the cloud data to a given cone model.
SampleConsensusModelCone defines a model for 3D cone segmentation.
SampleConsensusModelFromNormals represents the base model class for models that require the use of su...
Definition: sac_model.h:591
PointCloud represents the base class in PCL for storing collections of 3D points. ...
SacModel
Definition: model_types.h:48
Eigen::Vector3f getAxis() const
Get the axis along which we need to search for a cone direction.
pcl::PointCloud< PointT >::ConstPtr PointCloudConstPtr
Definition: sac_model.h:70
SampleConsensusModelCone(const SampleConsensusModelCone &source)
Copy constructor.
void projectPoints(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true)
Create a new point cloud with inliers projected onto the cone model.
SampleConsensusModelCone(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelCone.
std::string getName() const
Get a string representation of the name of this class.
SampleConsensusModel< PointT >::PointCloudPtr PointCloudPtr
pcl::SacModel getModelType() const
Return an unique id for this model (SACMODEL_CONE).
A point structure representing Euclidean xyz coordinates, and the RGB color.
void setAxis(const Eigen::Vector3f &ax)
Set the axis along which we need to search for a cone direction.
SampleConsensusModelCone(const PointCloudConstPtr &cloud, const std::vector< int > &indices, bool random=false)
Constructor for base SampleConsensusModelCone.
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...
virtual int countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold)
Count all the points which respect the given model coefficients as inliers.
boost::shared_ptr< SampleConsensusModelCone > Ptr
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition: sac_model.h:572