#include "ros/ros.h"
#include "pcl/console/parse.h"
#include "pcl/io/pcd_io.h"
#include "pcl/surface/convex_hull.h"
#include "pcl/filters/voxel_grid.h"
#include "pcl/filters/passthrough.h"
#include "pcl/filters/extract_indices.h"
#include "pcl/filters/project_inliers.h"
#include "pcl/filters/statistical_outlier_removal.h"
#include "pcl/sample_consensus/method_types.h"
#include "pcl/sample_consensus/impl/ransac.hpp"
#include "pcl/segmentation/sac_segmentation.h"
#include "pcl/segmentation/extract_clusters.h"
#include "pcl/segmentation/extract_polygonal_prism_data.h"
#include "pcl/visualization/pcl_visualizer.h"
#include "pcl_ias_sample_consensus/pcl_sac_model_orientation.h"
#include "dos_pcl/segmentation/door_detection_by_color_and_fixture.h"

Include dependency graph for segmentation_of_pointcloud.cpp:

Typedefs
typedef pcl::PointXYZ	PointT
Functions
bool	findBoxModel (boost::shared_ptr< const pcl::PointCloud< PointT > > cloud, pcl::PointCloud< pcl::Normal > nrmls, std::vector< double > &coeff, double eps_angle_=0.1, double success_probability_=0.99, int verbosity_level_=1)
	*
void	getAxesOrientedSurfaces (pcl::PointCloud< PointT > &input_cloud, pcl::PointCloud< pcl::Normal > &normals_cloud, Eigen::Vector3f axis, double epsilon_angle, double plane_threshold, int minimum_plane_inliers, int maximum_plane_iterations, int minimum_size_of_plane_cluster, double plane_inliers_clustering_tolerance, std::vector< pcl::PointCloud< PointT >::Ptr > &planar_surfaces, std::vector< std::string > &planar_surfaces_ids, std::vector< pcl::PointIndices::Ptr > &planar_surfaces_indices, std::vector< pcl::ModelCoefficients::Ptr > &planar_surfaces_coefficients, pcl::visualization::PCLVisualizer &viewer)
void	getMinAndMax (boost::shared_ptr< const pcl::PointCloud< PointT > > cloud_, Eigen::VectorXf model_coefficients, boost::shared_ptr< pcl::SACModelOrientation< pcl::Normal > > model, std::vector< int > &min_max_indices, std::vector< float > &min_max_distances)
	*
float	getRGB (float r, float g, float b)
int	main (int argc, char **argv)
	Main routine of the method. Segmentation of point cloud data.
Variables
double	center_radius = 0.085
	[meters]
bool	clean = false
double	cluster_tolerance = 0.020
	[meters]
double	color_radius = 0.050
double	epsilon_angle = 0.010
	[percentage]
bool	find_box_model = true
double	fixture_cluster_tolerance = 0.025
	[meters]
int	fixture_min_pts_per_cluster = 150
	[points]
double	handle_clustering_tolerance = 0.050
	[points]
double	height_of_ceiling = 0.050
	[percentage]
double	height_of_floor = 0.025
double	height_of_walls = 0.750
	[percentage]
double	init_radius = 0.035
int	maximum_plane_iterations = 1000
	[points]
int	min_pts_per_cluster = 150
int	minimum_plane_inliers = 1000
	[meters]
int	minimum_size_of_handle_cluster = 25
	[meters]
int	minimum_size_of_plane_cluster = 100
	[iterations]
double	plane_inliers_clustering_tolerance = 0.100
	[points]
double	plane_threshold = 0.100
	[radians]
bool	segmentation_by_color_and_fixture = false
int	size_of_points = 1
int	std_limit = 2
bool	step = false
	[points]
bool	verbose = false

Typedef Documentation

typedef pcl::PointXYZ PointT

Definition at line 68 of file segmentation_of_pointcloud.cpp.

Function Documentation

bool findBoxModel	(	boost::shared_ptr< const pcl::PointCloud< PointT > >	cloud,
		pcl::PointCloud< pcl::Normal >	nrmls,
		std::vector< double > &	coeff,
		double	eps_angle_ = `0.1`,
		double	success_probability_ = `0.99`,
		int	verbosity_level_ = `1`
	)

*

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

Definition at line 184 of file segmentation_of_pointcloud.cpp.

void getAxesOrientedSurfaces	(	pcl::PointCloud< PointT > &	input_cloud,
		pcl::PointCloud< pcl::Normal > &	normals_cloud,
		Eigen::Vector3f	axis,
		double	epsilon_angle,
		double	plane_threshold,
		int	minimum_plane_inliers,
		int	maximum_plane_iterations,
		int	minimum_size_of_plane_cluster,
		double	plane_inliers_clustering_tolerance,
		std::vector< pcl::PointCloud< PointT >::Ptr > &	planar_surfaces,
		std::vector< std::string > &	planar_surfaces_ids,
		std::vector< pcl::PointIndices::Ptr > &	planar_surfaces_indices,
		std::vector< pcl::ModelCoefficients::Ptr > &	planar_surfaces_coefficients,
		pcl::visualization::PCLVisualizer &	viewer
	)

*

Definition at line 391 of file segmentation_of_pointcloud.cpp.

void getMinAndMax	(	boost::shared_ptr< const pcl::PointCloud< PointT > >	cloud_,
		Eigen::VectorXf	model_coefficients,
		boost::shared_ptr< pcl::SACModelOrientation< pcl::Normal > >	model,
		std::vector< int > &	min_max_indices,
		std::vector< float > &	min_max_distances
	)