pcl_cloud_algos::RobustBoxEstimation Class Reference

#include <box_fit2_algo.h>

Inheritance diagram for pcl_cloud_algos::RobustBoxEstimation:

List of all members.

Public Member Functions
ros::Publisher	createPublisher (ros::NodeHandle &nh)
virtual bool	find_model (boost::shared_ptr< const pcl::PointCloud< pcl::PointXYZINormal > > cloud, std::vector< double > &coeff)
	Function for actual SaC-based model fitting.
void	getMinAndMax (Eigen::VectorXf model_coefficients, boost::shared_ptr< pcl::SACModelOrientation< pcl::PointXYZINormal > > model, std::vector< int > &min_max_indices, std::vector< float > &min_max_distances)
void	pre ()
std::vector< std::string >	requires ()
	RobustBoxEstimation ()
	Constructor and destructor.
Public Attributes
double	eps_angle_
	Inlier threshold for normals in radians.
double	success_probability_
	If it is not in the (0,1) interval, exhaustive search will be done, otherwise it will be the probability to be set for RANSAC (tradeoff between speed and accuracy).

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Detailed Description

Definition at line 40 of file box_fit2_algo.h.

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Constructor & Destructor Documentation

pcl_cloud_algos::RobustBoxEstimation::RobustBoxEstimation

(

)

[inline]

Constructor and destructor.

Definition at line 61 of file box_fit2_algo.h.

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Member Function Documentation

ros::Publisher pcl_cloud_algos::RobustBoxEstimation::createPublisher

(

ros::NodeHandle &

nh

)

[inline, virtual]

Reimplemented from pcl_cloud_algos::BoxEstimation.

Definition at line 84 of file box_fit2_algo.h.

bool RobustBoxEstimation::find_model	(	boost::shared_ptr< const pcl::PointCloud< pcl::PointXYZINormal > >	cloud,
		std::vector< double > &	coeff
	)		[virtual]

Function for actual SaC-based model fitting.

Parameters:

cloud	de-noisified input point cloud message with normals
coeff	box to-be-filled-in coefficients (15 elements): box center: cx, cy, cz, box dimensions: dx, dy, dz, box robust axes: e1_x, e1y, e1z, e2_x, e2y, e2z, e3_x, e3y, e3z

actual model fitting happens here

: inliers are actually indexes in the indices_ array, but that is not set (by default it has all the points in the correct order)

: making things transparent for the outside... not really needed

best_model_ contains actually the samples used to find the best model!

: making things transparent for the outside... not really needed

Reimplemented from pcl_cloud_algos::BoxEstimation.

Definition at line 111 of file box_fit2_algo.cpp.

void RobustBoxEstimation::getMinAndMax	(	Eigen::VectorXf	model_coefficients,
		boost::shared_ptr< pcl::SACModelOrientation< pcl::PointXYZINormal > >	model,
		std::vector< int > &	min_max_indices,
		std::vector< float > &	min_max_distances
	)

Definition at line 46 of file box_fit2_algo.cpp.

void RobustBoxEstimation::pre

(

)

[virtual]

Reimplemented from pcl_cloud_algos::BoxEstimation.

Definition at line 100 of file box_fit2_algo.cpp.

std::vector< std::string > RobustBoxEstimation::requires

(

)

[virtual]

Reimplemented from pcl_cloud_algos::BoxEstimation.

Definition at line 86 of file box_fit2_algo.cpp.

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Member Data Documentation

double pcl_cloud_algos::RobustBoxEstimation::eps_angle_

Inlier threshold for normals in radians.

Definition at line 48 of file box_fit2_algo.h.

double pcl_cloud_algos::RobustBoxEstimation::success_probability_

If it is not in the (0,1) interval, exhaustive search will be done, otherwise it will be the probability to be set for RANSAC (tradeoff between speed and accuracy).

Definition at line 55 of file box_fit2_algo.h.

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

• box_fit2_algo.h
• box_fit2_algo.cpp