Point Cloud Library (PCL)  1.7.0
Public Types | Public Member Functions | Protected Member Functions | Protected Attributes
pcl::SACSegmentation< PointT > Class Template Reference

SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation. More...

#include <pcl/segmentation/sac_segmentation.h>

+ Inheritance diagram for pcl::SACSegmentation< PointT >:

List of all members.

Public Types

typedef pcl::PointCloud< PointTPointCloud
typedef PointCloud::Ptr PointCloudPtr
typedef PointCloud::ConstPtr PointCloudConstPtr
typedef pcl::search::Search
< PointT >::Ptr 
SearchPtr
typedef SampleConsensus
< PointT >::Ptr 
SampleConsensusPtr
typedef SampleConsensusModel
< PointT >::Ptr 
SampleConsensusModelPtr

Public Member Functions

 SACSegmentation (bool random=false)
 Empty constructor.
virtual ~SACSegmentation ()
 Empty destructor.
void setModelType (int model)
 The type of model to use (user given parameter).
int getModelType () const
 Get the type of SAC model used.
SampleConsensusPtr getMethod () const
 Get a pointer to the SAC method used.
SampleConsensusModelPtr getModel () const
 Get a pointer to the SAC model used.
void setMethodType (int method)
 The type of sample consensus method to use (user given parameter).
int getMethodType () const
 Get the type of sample consensus method used.
void setDistanceThreshold (double threshold)
 Distance to the model threshold (user given parameter).
double getDistanceThreshold () const
 Get the distance to the model threshold.
void setMaxIterations (int max_iterations)
 Set the maximum number of iterations before giving up.
int getMaxIterations () const
 Get maximum number of iterations before giving up.
void setProbability (double probability)
 Set the probability of choosing at least one sample free from outliers.
double getProbability () const
 Get the probability of choosing at least one sample free from outliers.
void setOptimizeCoefficients (bool optimize)
 Set to true if a coefficient refinement is required.
bool getOptimizeCoefficients () const
 Get the coefficient refinement internal flag.
void setRadiusLimits (const double &min_radius, const double &max_radius)
 Set the minimum and maximum allowable radius limits for the model (applicable to models that estimate a radius)
void getRadiusLimits (double &min_radius, double &max_radius)
 Get the minimum and maximum allowable radius limits for the model as set by the user.
void setSamplesMaxDist (const double &radius, SearchPtr search)
 Set the maximum distance allowed when drawing random samples.
void getSamplesMaxDist (double &radius)
 Get maximum distance allowed when drawing random samples.
void setAxis (const Eigen::Vector3f &ax)
 Set the axis along which we need to search for a model perpendicular to.
Eigen::Vector3f getAxis () const
 Get the axis along which we need to search for a model perpendicular to.
void setEpsAngle (double ea)
 Set the angle epsilon (delta) threshold.
double getEpsAngle () const
 Get the epsilon (delta) model angle threshold in radians.
virtual void segment (PointIndices &inliers, ModelCoefficients &model_coefficients)
 Base method for segmentation of a model in a PointCloud given by <setInputCloud (), setIndices ()>

Protected Member Functions

virtual bool initSACModel (const int model_type)
 Initialize the Sample Consensus model and set its parameters.
virtual void initSAC (const int method_type)
 Initialize the Sample Consensus method and set its parameters.
virtual std::string getClassName () const
 Class get name method.

Protected Attributes

SampleConsensusModelPtr model_
 The model that needs to be segmented.
SampleConsensusPtr sac_
 The sample consensus segmentation method.
int model_type_
 The type of model to use (user given parameter).
int method_type_
 The type of sample consensus method to use (user given parameter).
double threshold_
 Distance to the model threshold (user given parameter).
bool optimize_coefficients_
 Set to true if a coefficient refinement is required.
double radius_min_
 The minimum and maximum radius limits for the model.
double radius_max_
double samples_radius_
 The maximum distance of subsequent samples from the first (radius search)
SearchPtr samples_radius_search_
 The search object for picking subsequent samples using radius search.
double eps_angle_
 The maximum allowed difference between the model normal and the given axis.
Eigen::Vector3f axis_
 The axis along which we need to search for a model perpendicular to.
int max_iterations_
 Maximum number of iterations before giving up (user given parameter).
double probability_
 Desired probability of choosing at least one sample free from outliers (user given parameter).
bool random_
 Set to true if we need a random seed.

Detailed Description

template<typename PointT>
class pcl::SACSegmentation< PointT >

SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation.

Author:
Radu Bogdan Rusu

Definition at line 65 of file sac_segmentation.h.


Member Typedef Documentation

template<typename PointT>
typedef pcl::PointCloud<PointT> pcl::SACSegmentation< PointT >::PointCloud

Reimplemented from pcl::PCLBase< PointT >.

Reimplemented in pcl::SACSegmentationFromNormals< PointT, PointNT >.

Definition at line 74 of file sac_segmentation.h.

template<typename PointT>
typedef PointCloud::ConstPtr pcl::SACSegmentation< PointT >::PointCloudConstPtr

Reimplemented from pcl::PCLBase< PointT >.

Reimplemented in pcl::SACSegmentationFromNormals< PointT, PointNT >.

Definition at line 76 of file sac_segmentation.h.

template<typename PointT>
typedef PointCloud::Ptr pcl::SACSegmentation< PointT >::PointCloudPtr

Reimplemented from pcl::PCLBase< PointT >.

Reimplemented in pcl::SACSegmentationFromNormals< PointT, PointNT >.

Definition at line 75 of file sac_segmentation.h.

template<typename PointT>
typedef SampleConsensusModel<PointT>::Ptr pcl::SACSegmentation< PointT >::SampleConsensusModelPtr

Reimplemented in pcl::SACSegmentationFromNormals< PointT, PointNT >.

Definition at line 80 of file sac_segmentation.h.

template<typename PointT>
typedef SampleConsensus<PointT>::Ptr pcl::SACSegmentation< PointT >::SampleConsensusPtr

Reimplemented in pcl::SACSegmentationFromNormals< PointT, PointNT >.

Definition at line 79 of file sac_segmentation.h.

template<typename PointT>
typedef pcl::search::Search<PointT>::Ptr pcl::SACSegmentation< PointT >::SearchPtr

Definition at line 77 of file sac_segmentation.h.


Constructor & Destructor Documentation

template<typename PointT>
pcl::SACSegmentation< PointT >::SACSegmentation ( bool  random = false) [inline]

Empty constructor.

Parameters:
[in]randomif true set the random seed to the current time, else set to 12345 (default: false)

Definition at line 85 of file sac_segmentation.h.

template<typename PointT>
virtual pcl::SACSegmentation< PointT >::~SACSegmentation ( ) [inline, virtual]

Empty destructor.

Definition at line 105 of file sac_segmentation.h.


Member Function Documentation

template<typename PointT>
Eigen::Vector3f pcl::SACSegmentation< PointT >::getAxis ( ) const [inline]

Get the axis along which we need to search for a model perpendicular to.

Definition at line 226 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::axis_.

template<typename PointT>
virtual std::string pcl::SACSegmentation< PointT >::getClassName ( ) const [inline, protected, virtual]

Class get name method.

Reimplemented in pcl::SACSegmentationFromNormals< PointT, PointNT >.

Definition at line 302 of file sac_segmentation.h.

template<typename PointT>
double pcl::SACSegmentation< PointT >::getDistanceThreshold ( ) const [inline]

Get the distance to the model threshold.

Definition at line 143 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::threshold_.

template<typename PointT>
double pcl::SACSegmentation< PointT >::getEpsAngle ( ) const [inline]

Get the epsilon (delta) model angle threshold in radians.

Definition at line 236 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::eps_angle_.

template<typename PointT>
int pcl::SACSegmentation< PointT >::getMaxIterations ( ) const [inline]

Get maximum number of iterations before giving up.

Definition at line 153 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::max_iterations_.

template<typename PointT>
SampleConsensusPtr pcl::SACSegmentation< PointT >::getMethod ( ) const [inline]

Get a pointer to the SAC method used.

Definition at line 119 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::sac_.

template<typename PointT>
int pcl::SACSegmentation< PointT >::getMethodType ( ) const [inline]

Get the type of sample consensus method used.

Definition at line 133 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::method_type_.

template<typename PointT>
SampleConsensusModelPtr pcl::SACSegmentation< PointT >::getModel ( ) const [inline]

Get a pointer to the SAC model used.

Definition at line 123 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::model_.

template<typename PointT>
int pcl::SACSegmentation< PointT >::getModelType ( ) const [inline]

Get the type of SAC model used.

Definition at line 115 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::model_type_.

template<typename PointT>
bool pcl::SACSegmentation< PointT >::getOptimizeCoefficients ( ) const [inline]

Get the coefficient refinement internal flag.

Definition at line 173 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::optimize_coefficients_.

template<typename PointT>
double pcl::SACSegmentation< PointT >::getProbability ( ) const [inline]

Get the probability of choosing at least one sample free from outliers.

Definition at line 163 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::probability_.

template<typename PointT>
void pcl::SACSegmentation< PointT >::getRadiusLimits ( double &  min_radius,
double &  max_radius 
) [inline]

Get the minimum and maximum allowable radius limits for the model as set by the user.

Parameters:
[out]min_radiusthe resultant minimum radius model
[out]max_radiusthe resultant maximum radius model

Definition at line 192 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::radius_max_, and pcl::SACSegmentation< PointT >::radius_min_.

template<typename PointT>
void pcl::SACSegmentation< PointT >::getSamplesMaxDist ( double &  radius) [inline]

Get maximum distance allowed when drawing random samples.

Parameters:
[out]radiusthe maximum distance (L2 norm)

Definition at line 213 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::samples_radius_.

template<typename PointT >
void pcl::SACSegmentation< PointT >::initSAC ( const int  method_type) [protected, virtual]

Initialize the Sample Consensus method and set its parameters.

Parameters:
[in]method_typethe type of SAC method to be used

Definition at line 270 of file sac_segmentation.hpp.

template<typename PointT >
bool pcl::SACSegmentation< PointT >::initSACModel ( const int  model_type) [protected, virtual]
template<typename PointT >
void pcl::SACSegmentation< PointT >::segment ( PointIndices inliers,
ModelCoefficients model_coefficients 
) [virtual]

Base method for segmentation of a model in a PointCloud given by <setInputCloud (), setIndices ()>

Parameters:
[in]inliersthe resultant point indices that support the model found (inliers)
[out]model_coefficientsthe resultant model coefficients

Definition at line 75 of file sac_segmentation.hpp.

References pcl::PointIndices::header, pcl::ModelCoefficients::header, pcl::PointIndices::indices, and pcl::ModelCoefficients::values.

template<typename PointT>
void pcl::SACSegmentation< PointT >::setAxis ( const Eigen::Vector3f &  ax) [inline]

Set the axis along which we need to search for a model perpendicular to.

Parameters:
[in]axthe axis along which we need to search for a model perpendicular to

Definition at line 222 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::axis_.

template<typename PointT>
void pcl::SACSegmentation< PointT >::setDistanceThreshold ( double  threshold) [inline]

Distance to the model threshold (user given parameter).

Parameters:
[in]thresholdthe distance threshold to use

Definition at line 139 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::threshold_.

template<typename PointT>
void pcl::SACSegmentation< PointT >::setEpsAngle ( double  ea) [inline]

Set the angle epsilon (delta) threshold.

Parameters:
[in]eathe maximum allowed difference between the model normal and the given axis in radians.

Definition at line 232 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::eps_angle_.

template<typename PointT>
void pcl::SACSegmentation< PointT >::setMaxIterations ( int  max_iterations) [inline]

Set the maximum number of iterations before giving up.

Parameters:
[in]max_iterationsthe maximum number of iterations the sample consensus method will run

Definition at line 149 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::max_iterations_.

template<typename PointT>
void pcl::SACSegmentation< PointT >::setMethodType ( int  method) [inline]

The type of sample consensus method to use (user given parameter).

Parameters:
[in]methodthe method type (check method_types.h)

Definition at line 129 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::method_type_.

template<typename PointT>
void pcl::SACSegmentation< PointT >::setModelType ( int  model) [inline]

The type of model to use (user given parameter).

Parameters:
[in]modelthe model type (check model_types.h)

Definition at line 111 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::model_type_.

template<typename PointT>
void pcl::SACSegmentation< PointT >::setOptimizeCoefficients ( bool  optimize) [inline]

Set to true if a coefficient refinement is required.

Parameters:
[in]optimizetrue for enabling model coefficient refinement, false otherwise

Definition at line 169 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::optimize_coefficients_.

template<typename PointT>
void pcl::SACSegmentation< PointT >::setProbability ( double  probability) [inline]

Set the probability of choosing at least one sample free from outliers.

Parameters:
[in]probabilitythe model fitting probability

Definition at line 159 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::probability_.

template<typename PointT>
void pcl::SACSegmentation< PointT >::setRadiusLimits ( const double &  min_radius,
const double &  max_radius 
) [inline]

Set the minimum and maximum allowable radius limits for the model (applicable to models that estimate a radius)

Parameters:
[in]min_radiusthe minimum radius model
[in]max_radiusthe maximum radius model

Definition at line 181 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::radius_max_, and pcl::SACSegmentation< PointT >::radius_min_.

template<typename PointT>
void pcl::SACSegmentation< PointT >::setSamplesMaxDist ( const double &  radius,
SearchPtr  search 
) [inline]

Set the maximum distance allowed when drawing random samples.

Parameters:
[in]radiusthe maximum distance (L2 norm)

Definition at line 202 of file sac_segmentation.h.

References pcl::SACSegmentation< PointT >::samples_radius_, and pcl::SACSegmentation< PointT >::samples_radius_search_.


Member Data Documentation

template<typename PointT>
Eigen::Vector3f pcl::SACSegmentation< PointT >::axis_ [protected]

The axis along which we need to search for a model perpendicular to.

Definition at line 289 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getAxis(), and pcl::SACSegmentation< PointT >::setAxis().

template<typename PointT>
double pcl::SACSegmentation< PointT >::eps_angle_ [protected]

The maximum allowed difference between the model normal and the given axis.

Definition at line 286 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getEpsAngle(), and pcl::SACSegmentation< PointT >::setEpsAngle().

template<typename PointT>
int pcl::SACSegmentation< PointT >::max_iterations_ [protected]

Maximum number of iterations before giving up (user given parameter).

Definition at line 292 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getMaxIterations(), and pcl::SACSegmentation< PointT >::setMaxIterations().

template<typename PointT>
int pcl::SACSegmentation< PointT >::method_type_ [protected]

The type of sample consensus method to use (user given parameter).

Definition at line 268 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getMethodType(), and pcl::SACSegmentation< PointT >::setMethodType().

template<typename PointT>
SampleConsensusModelPtr pcl::SACSegmentation< PointT >::model_ [protected]

The model that needs to be segmented.

Definition at line 259 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getModel().

template<typename PointT>
int pcl::SACSegmentation< PointT >::model_type_ [protected]

The type of model to use (user given parameter).

Definition at line 265 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getModelType(), and pcl::SACSegmentation< PointT >::setModelType().

template<typename PointT>
bool pcl::SACSegmentation< PointT >::optimize_coefficients_ [protected]

Set to true if a coefficient refinement is required.

Definition at line 274 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getOptimizeCoefficients(), and pcl::SACSegmentation< PointT >::setOptimizeCoefficients().

template<typename PointT>
double pcl::SACSegmentation< PointT >::probability_ [protected]

Desired probability of choosing at least one sample free from outliers (user given parameter).

Definition at line 295 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getProbability(), and pcl::SACSegmentation< PointT >::setProbability().

template<typename PointT>
double pcl::SACSegmentation< PointT >::radius_max_ [protected]
template<typename PointT>
double pcl::SACSegmentation< PointT >::radius_min_ [protected]

The minimum and maximum radius limits for the model.

Applicable to all models that estimate a radius.

Definition at line 277 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getRadiusLimits(), and pcl::SACSegmentation< PointT >::setRadiusLimits().

template<typename PointT>
bool pcl::SACSegmentation< PointT >::random_ [protected]

Set to true if we need a random seed.

Definition at line 298 of file sac_segmentation.h.

template<typename PointT>
SampleConsensusPtr pcl::SACSegmentation< PointT >::sac_ [protected]

The sample consensus segmentation method.

Definition at line 262 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getMethod().

template<typename PointT>
double pcl::SACSegmentation< PointT >::samples_radius_ [protected]

The maximum distance of subsequent samples from the first (radius search)

Definition at line 280 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getSamplesMaxDist(), and pcl::SACSegmentation< PointT >::setSamplesMaxDist().

template<typename PointT>
SearchPtr pcl::SACSegmentation< PointT >::samples_radius_search_ [protected]

The search object for picking subsequent samples using radius search.

Definition at line 283 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::setSamplesMaxDist().

template<typename PointT>
double pcl::SACSegmentation< PointT >::threshold_ [protected]

Distance to the model threshold (user given parameter).

Definition at line 271 of file sac_segmentation.h.

Referenced by pcl::SACSegmentation< PointT >::getDistanceThreshold(), and pcl::SACSegmentation< PointT >::setDistanceThreshold().


The documentation for this class was generated from the following files: