/home/laradmin/lar/perception/planarobstacles/mtt/include/mtt/pmht.h File Reference

Header for the Multi-Hypothesis Tracking data association algorithm. More...

#include <ros/ros.h>
#include <mtt/tree.hh>
#include <mtt/tree_util.hh>
#include <colormap/colormap.h>
#include <laser_geometry/laser_geometry.h>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/surface/mls.h>
#include <Eigen/Dense>
#include <Eigen/Cholesky>
#include <visualization_msgs/Marker.h>
#include <visualization_msgs/MarkerArray.h>
#include <graphviz/gvc.h>
#include <graphviz/types.h>
#include <stdio.h>
#include <assert.h>
#include <signal.h>
#include "geom.h"
#include "gvcext.h"
#include "pathgeom.h"
#include "textpara.h"
#include <cgraph.h>
#include "usershape.h"
#include <mtt/types_declaration.h>
#include <mtt/graph_wrapper.h>
#include <mtt/mht.h>

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Functions
void	CreateMarkers (vector< visualization_msgs::Marker > &marker_vector, vector< t_clusterPtr > &clusters)
	Create markers for Rviz.
void	CreateObjects (vector< t_pointPtr > &data, vector< t_clusterPtr > &clusters, double clustering_distance)
	This function converts a set of points into groups (aka clusters).
MatrixXd	EllipseParametric (Matrix2d &cov, Vector2d &mu, double MahThreshold)
	Create a parametric ellipse from covariance matrix.
double	Euclidean_distance (t_point &p1, t_point &p2)
	Calculate the Euclidean distance from point to point.
double	Euclidean_distance (t_nodePtr &node, t_clusterPtr &cluster)
	Calculate the Euclidean distance from node to cluster.
vector< t_clusterPtr >::iterator	GetClosestCandidate (t_nodePtr &node, vector< t_clusterPtr > &clusters)
	Get the closest candidate to the node from all the clusters.
void	gvconfig_plugin_install_from_library (GVC_t , char , gvplugin_library_t *)
double	Mahalanobis (Vector2d &y, Vector2d &mean, Matrix2d &cov)
	Calculate the Mahalanobis distance.
double	Mahalanobis_distance (t_nodePtr &node, t_clusterPtr &cluster)
	Calculate the distance between a node and a cluster.
void	PointCloud2ToVector (const sensor_msgs::PointCloud2 &cloud, vector< t_pointPtr > &data)
	Convert from a point cloud representation to a stl vector of shared pointers to the class t_point.

Detailed Description

Header for the Multi-Hypothesis Tracking data association algorithm.

For additional info on the Mht algorithm check here.

Definition in file pmht.h.

Function Documentation

void CreateMarkers	(	vector< visualization_msgs::Marker > &	marker_vector,
		vector< t_clusterPtr > &	clusters
	)

Create markers for Rviz.

Definition at line 379 of file pmht.cpp.

void CreateObjects	(	vector< t_pointPtr > &	data,
		vector< t_clusterPtr > &	clusters,
		double	clustering_distance
	)

This function converts a set of points into groups (aka clusters).

The clustering algorithm is solely based on the distance between consecutive points in the polar grid.
It copies the support points of the cluster into the own cluster so no reference to the original data is needed not even desired.

Parameters:

	data	vector of data
	clusters	vector of clusters (this is the output so it will be filled)
	clustering_distance	maximum distance between two points for them to be in the same cluster

Definition at line 525 of file pmht.cpp.

MatrixXd EllipseParametric	(	Matrix2d &	cov,
		Vector2d &	mu,
		double	MahThreshold
	)

Create a parametric ellipse from covariance matrix.

This function creates a parametric ellipse from a covariance matrix, the mean values and a threshold. The main axes directions are calculated using the eigenvectors of the covariance matrix, then a crawling algorithm detects the threshold points obtaining the lengths of the axes. After that the generic parametric equation is used to calculate points in the threshold boundary.

Parameters:

	cov	covariance matrix
	mu	mean values
	MahThreshold	Mahalanobis threshold to use

Returns:: the xy points of the ellipse, the last points is equal to the first so the ellipse closes

Definition at line 125 of file pmht.cpp.

double Euclidean_distance	(	t_point &	p1,
		t_point &	p2
	)

Calculate the Euclidean distance from point to point.

Calculate the euclidean distance from a point to a point

Parameters:

	p1	point 1
	p2	point 2

Returns:: the euclidean distance

Definition at line 156 of file pmht.h.

double Euclidean_distance	(	t_nodePtr &	node,
		t_clusterPtr &	cluster
	)

Calculate the Euclidean distance from node to cluster.

Calculate the euclidean distance from a node and a cluster.

Parameters:

	node	Node, the coordinates in x_p point of the node will be used
	cluster	Cluster, the centroid coordinates will be used

Returns:: the euclidean distance

Definition at line 143 of file pmht.h.

vector<t_clusterPtr>::iterator GetClosestCandidate	(	t_nodePtr &	node,
		vector< t_clusterPtr > &	clusters
	)

Get the closest candidate to the node from all the clusters.

This function calls the CalculateDistance function to do the real node to cluster evaluation.

Parameters:

	node	node to use
	clusters	clusters to search

Returns:: iterator to a member of the clusters

Definition at line 601 of file pmht.cpp.

void gvconfig_plugin_install_from_library	(	GVC_t *	,
		char *	,
		gvplugin_library_t *
	)

double Mahalanobis	(	Vector2d &	y,
		Vector2d &	mean,
		Matrix2d &	cov
	)

Calculate the Mahalanobis distance.

Calculate the Mahalanobis distance for a measurement y from a set with a given mean and covariance.

Parameters:

	y	measurement
	mean	mean value of the variable
	cov	covariance of the variable

Returns:: the Mahalanobis distance

Definition at line 572 of file pmht.cpp.

double Mahalanobis_distance	(	t_nodePtr &	node,
		t_clusterPtr &	cluster
	)

Calculate the distance between a node and a cluster.

This function uses the Mahalanobis distance between the node position estimation and the cluster position.

Parameters:

	node	tree node to use
	cluster	cluster to use

Returns:: the Mahalanobis distance

Definition at line 580 of file pmht.cpp.

void PointCloud2ToVector	(	const sensor_msgs::PointCloud2 &	cloud,
		vector< t_pointPtr > &	data
	)

Convert from a point cloud representation to a stl vector of shared pointers to the class t_point.

This function first puts all the points in a polar grid representation and reorders them to be angularly sequenced.
This function has an important parameter which is the angular resolution of the grid that should be a ros param.

Todo:: Convert the current hard coded angular resolution of the grid (vulgar grid spacing).

Parameters:

	cloud	input point cloud
	data	stl vector of boost::shared_ptr of the class t_point

Definition at line 464 of file pmht.cpp.

/home/laradmin/lar/perception/planarobstacles/mtt/include/mtt/pmht.h File Reference

Functions

Detailed Description

Function Documentation