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Hi there, I am new here... So, I need to write a ROS Node to solve a problem using the Euclidean Cluster Extractionn PCL, but I don't know whats exactly is wrong. Please, help me to understand it. And thank you a lot for that!

#include <ros ros.h=""> #include <nodelet nodelet.h=""> #include <sensor_msgs pointcloud2.h=""> #include <pcl_conversions pcl_conversions.h=""> #include <pcl point_cloud.h=""> #include <pcl point_types.h=""> #include <boost foreach.hpp=""> #include <sstream> #include "novinho_laser/Vai.h" #include <pcl modelcoefficients.h=""> #include <pcl point_types.h=""> #include <pcl io="" pcd_io.h=""> #include <pcl filters="" extract_indices.h=""> #include <pcl filters="" voxel_grid.h=""> #include <pcl features="" normal_3d.h=""> #include <pcl kdtree="" kdtree.h=""> #include <pcl sample_consensus="" method_types.h=""> #include <pcl sample_consensus="" model_types.h=""> #include <pcl segmentation="" sac_segmentation.h=""> #include <pcl segmentation="" extract_clusters.h=""> ros::Publisher pub;

using namespace pcl; using namespace std;

typedef pcl::PointXYZ PointT; typedef pcl::PointCloud<pointt> PointCloudT;

void cloud_cb ( const sensor_msgs::PointCloud2 input ) { pcl::PCLPointCloud2 pcl_pc; pcl_conversions::toPCL ( input, pcl_pc ); pcl::PointCloud<pcl::pointxyz> cloud, cloud_f; pcl::fromPCLPointCloud2 ( pcl_pc, cloud );

//!< Create the filtering object: downsample the dataset using a leaf size of 1cm pcl::VoxelGrid<pcl::pointxyz> vg; pcl::PointCloud<pcl::pointxyz>::Ptr cloud_filtered ( new pcl::PointCloud<pcl::pointxyz> ); vg.setInputCloud ( cloud_f ); // ...cloud -> cloud_f vg.setLeafSize ( 0.01f, 0.01f, 0.01f ); vg.filter ( *cloud_filtered );

//!< Create the segmentation object for the planar model and set all the parameters pcl::SACSegmentation<pcl::pointxyz> seg; pcl::PointIndices::Ptr inliers ( new pcl::PointIndices ); pcl::ModelCoefficients::Ptr coefficients ( new pcl::ModelCoefficients ); pcl::PointCloud<pcl::pointxyz>::Ptr cloud_plane ( new pcl::PointCloud<pcl::pointxyz> ( ) );

seg.setOptimizeCoefficients ( true ); seg.setModelType ( pcl::SACMODEL_PLANE ); seg.setMethodType ( pcl::SAC_RANSAC ); seg.setMaxIterations ( 100 ); seg.setDistanceThreshold ( 0.02 );

int i = 0, nr_points = ( int ) cloud_filtered->points.size ( ); while ( cloud_filtered->points.size ( ) > 0.3 * nr_points ) { //!< Segment the largest planar component from the remaining cloud seg.setInputCloud ( cloud_filtered ); seg.segment ( *inliers, *coefficients ); if (inliers->indices.size ( ) == 0 ) break;

//!< Extract the planar inliers from the input cloud
pcl::ExtractIndices<pcl::PointXYZ> extract;
extract.setInputCloud ( cloud_filtered );
extract.setIndices    ( inliers );
extract.setNegative   ( false );

//!< Get the points associated with the planar surface
extract.filter ( *cloud_plane );

//!< Remove the planar inliers, extract the rest
extract.setNegative ( true );
extract.filter ( *cloud_f  );
*cloud_filtered = *cloud_f;

}

//!< Creating the KdTree object for the search method of the extraction pcl::search::KdTree<pcl::pointxyz>::Ptr tree ( new pcl::search::KdTree<pcl::pointxyz> ); tree->setInputCloud ( cloud_filtered );

std::vector<pcl::pointindices> cluster_indices;

pcl::EuclideanClusterExtraction<pcl::pointxyz> ec; ec.setClusterTolerance ( 0.02 ); // 2cm ec.setMinClusterSize ( 100 ); ec.setMaxClusterSize ( 25000 ); ec.setSearchMethod ( tree ); ec.setInputCloud ( cloud_filtered ); ec.extract ( cluster_indices );

for ( std::vector<pcl::pointindices>::const_iterator it = cluster_indices.begin ( ); it != cluster_indices.end ( ); ++it ) { pcl::PointCloud<pcl::pointxyz>::Ptr cloud_cluster ( new pcl::PointCloud<pcl::pointxyz> ); for ( std::vector<int>::const_iterator pit = it->indices.begin ( ); pit != it->indices.end ( ); ++pit ) cloud_cluster->points.push_back ( cloud_filtered->points [ *pit ] ); cloud_cluster->width = cloud_cluster->points.size ( ); cloud_cluster->height = 1; cloud_cluster->is_dense = true; }

float x = 0.0, y = 0.0, z = 1e6; unsigned int n = 0; //!< - Number of points observed

BOOST_FOREACH ( const pcl::PointXYZ& pt, cloud_cluster ...