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#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 <boost/lexical_cast.hpp>
// #include <boost/format.hpp>

#include <graphviz/gvc.h>
#include <graphviz/types.h>
#include <graphviz/gvplugin.h>

#include <mtt/types_declaration.h>
#include <mtt/graph_wrapper.h>
#include <mtt/mht.h>

extern "C" {
        extern void gvconfig_plugin_install_from_library(GVC_t*,char*,gvplugin_library_t*);
}

using namespace std;
using namespace ros;


//Prototypes

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

void CreateMarkers(vector<visualization_msgs::Marker>& marker_vector,vector<t_clusterPtr>&clusters);
void PointCloud2ToVector(const sensor_msgs::PointCloud2& cloud,vector<t_pointPtr>& data);

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

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

double Mahalanobis_distance(t_nodePtr&node,t_clusterPtr&cluster);

double Euclidean_distance(t_nodePtr&node,t_clusterPtr&cluster)
{
        return sqrt( pow(node->x_p(0)-cluster->centroid.x,2) + pow(node->x_p(1)-cluster->centroid.y,2));
}

double Euclidean_distance(t_point&p1,t_point&p2)
{
        return sqrt( pow(p1.x-p2.x,2) + pow(p1.y-p2.y,2));
}

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

---

mtt
Author(s): Jorge Almeida
autogenerated on Wed Jul 23 04:34:56 2014