main_naive.cpp

Go to the documentation of this file.

#include "kf_tracker/CKalmanFilter.h"
#include "kf_tracker/featureDetection.h"
#include "opencv2/video/tracking.hpp"
#include "pcl_ros/point_cloud.h"
#include <algorithm>
#include <fstream>
#include <geometry_msgs/Point.h>
#include <iostream>
#include <iterator>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/video/video.hpp>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <ros/ros.h>
#include <std_msgs/Float32MultiArray.h>
#include <std_msgs/Int32MultiArray.h>
#include <string.h>
 
#include <pcl/common/geometry.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl_conversions/pcl_conversions.h>
#include <sensor_msgs/PointCloud2.h>
 
using namespace std;
using namespace cv;
 
ros::Publisher objID_pub;
 
// KF init
int stateDim = 4; // [x,y,v_x,v_y]//,w,h]
int measDim = 2;  // [z_x,z_y,z_w,z_h]
int ctrlDim = 0;
cv::KalmanFilter KF0(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF1(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF2(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF3(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF4(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF5(stateDim, measDim, ctrlDim, CV_32F);
 
ros::Publisher pub_cluster0;
ros::Publisher pub_cluster1;
ros::Publisher pub_cluster2;
ros::Publisher pub_cluster3;
ros::Publisher pub_cluster4;
ros::Publisher pub_cluster5;
 
std::vector<geometry_msgs::Point> prevClusterCenters;
 
cv::Mat state(stateDim, 1, CV_32F);
cv::Mat_<float> measurement(2, 1);
 
std::vector<int> objID; // Output of the data association using KF
                        // measurement.setTo(Scalar(0));
 
bool firstFrame = true;
 
// calculate euclidean distance of two points
double euclidean_distance(geometry_msgs::Point &p1, geometry_msgs::Point &p2) {
  return sqrt((p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y - p2.y) +
              (p1.z - p2.z) * (p1.z - p2.z));
}
/*
//Count unique object IDs. just to make sure same ID has not been assigned to
two KF_Trackers. int countIDs(vector<int> v)
{
    transform(v.begin(), v.end(), v.begin(), abs); // O(n) where n =
distance(v.end(), v.begin()) sort(v.begin(), v.end()); // Average case O(n log
n), worst case O(n^2) (usually implemented as quicksort.
    // To guarantee worst case O(n log n) replace with make_heap, then
sort_heap.
 
    // Unique will take a sorted range, and move things around to get duplicated
    // items to the back and returns an iterator to the end of the unique
section of the range auto unique_end = unique(v.begin(), v.end()); // Again n
comparisons return distance(unique_end, v.begin()); // Constant time for random
access iterators (like vector's)
}
*/
 
/*
 
objID: vector containing the IDs of the clusters that should be associated with
each KF_Tracker objID[0] corresponds to KFT0, objID[1] corresponds to KFT1 etc.
*/
void KFT(const std_msgs::Float32MultiArray ccs) {
 
  // First predict, to update the internal statePre variable
 
  std::vector<cv::Mat> pred{KF0.predict(), KF1.predict(), KF2.predict(),
                            KF3.predict(), KF4.predict(), KF5.predict()};
  // cout<<"Pred successfull\n";
 
  // cv::Point predictPt(prediction.at<float>(0),prediction.at<float>(1));
  // cout<<"Prediction 1
  // ="<<prediction.at<float>(0)<<","<<prediction.at<float>(1)<<"\n";
 
  // Get measurements
  // Extract the position of the clusters forom the multiArray. To check if the
  // data coming in, check the .z (every third) coordinate and that will be 0.0
  std::vector<geometry_msgs::Point> clusterCenters; // clusterCenters
 
  int i = 0;
  for (std::vector<float>::const_iterator it = ccs.data.begin();
       it != ccs.data.end(); it += 3) {
    geometry_msgs::Point pt;
    pt.x = *it;
    pt.y = *(it + 1);
    pt.z = *(it + 2);
 
    clusterCenters.push_back(pt);
  }
 
  //  cout<<"CLusterCenters Obtained"<<"\n";
  std::vector<geometry_msgs::Point> KFpredictions;
  i = 0;
  for (auto it = pred.begin(); it != pred.end(); it++) {
    geometry_msgs::Point pt;
    pt.x = (*it).at<float>(0);
    pt.y = (*it).at<float>(1);
    pt.z = (*it).at<float>(2);
 
    KFpredictions.push_back(pt);
  }
  // cout<<"Got predictions"<<"\n";
 
  /* Original Version using Kalman filter prediction
 
  // Find the cluster that is more probable to be belonging to a given KF.
  objID.clear();//Clear the objID vector
  for(int filterN=0;filterN<6;filterN++)
  {
      std::vector<float> distVec;
      for(int n=0;n<6;n++)
          distVec.push_back(euclidean_distance(KFpredictions[filterN],clusterCenters[n]));
 
    //
 cout<<"distVec[="<<distVec[0]<<","<<distVec[1]<<","<<distVec[2]<<","<<distVec[3]<<","<<distVec[4]<<","<<distVec[5]<<"\n";
      objID.push_back(std::distance(distVec.begin(),min_element(distVec.begin(),distVec.end())));
     // cout<<"MinD for
 filter"<<filterN<<"="<<*min_element(distVec.begin(),distVec.end())<<"\n";
 
  }
 
 // cout<<"Got object IDs"<<"\n";
  //countIDs(objID);// for verif/corner cases
    Original version using kalman filter prediction
  */
  // display objIDs
  /* DEBUG
    cout<<"objID= ";
    for(auto it=objID.begin();it!=objID.end();it++)
        cout<<*it<<" ,";
    cout<<"\n";
    */
 
  /* Naive version without using kalman filter */
  objID.clear(); // Clear the objID vector
  for (int filterN = 0; filterN < 6; filterN++) {
    std::vector<float> distVec;
    for (int n = 0; n < 6; n++)
      distVec.push_back(
          euclidean_distance(prevClusterCenters[n], clusterCenters[n]));
 
    // cout<<"distVec[="<<distVec[0]<<","<<distVec[1]<<","<<distVec[2]<<","<<distVec[3]<<","<<distVec[4]<<","<<distVec[5]<<"\n";
    objID.push_back(std::distance(distVec.begin(),
                                  min_element(distVec.begin(), distVec.end())));
    // cout<<"MinD for
    // filter"<<filterN<<"="<<*min_element(distVec.begin(),distVec.end())<<"\n";
  }
  /* Naive version without kalman filter */
  // prevClusterCenters.clear();
  // Set the current associated clusters to the prevClusterCenters
  for (int i = 0; i < 6; i++) {
    prevClusterCenters[objID.at(i)] = clusterCenters.at(i);
  }
 
  /* Naive version without kalman filter */
 
  /* Naive version without using kalman filter */
 
  std_msgs::Int32MultiArray obj_id;
  for (auto it = objID.begin(); it != objID.end(); it++)
    obj_id.data.push_back(*it);
  objID_pub.publish(obj_id);
  // convert clusterCenters from geometry_msgs::Point to floats
  std::vector<std::vector<float>> cc;
  for (int i = 0; i < clusterCenters.size(); i++) {
    vector<float> pt;
    pt.push_back(clusterCenters[i].x);
    pt.push_back(clusterCenters[i].y);
    pt.push_back(clusterCenters[i].z);
 
    cc.push_back(pt);
  }
  // cout<<"cc[5][0]="<<cc[5].at(0)<<"cc[5][1]="<<cc[5].at(1)<<"cc[5][2]="<<cc[5].at(2)<<"\n";
  float meas0[3] = {cc[0].at(0), cc[0].at(1)};
  float meas1[3] = {cc[1].at(0), cc[1].at(1)};
  float meas2[3] = {cc[2].at(0), cc[2].at(1)};
  float meas3[3] = {cc[3].at(0), cc[3].at(1)};
  float meas4[3] = {cc[4].at(0), cc[4].at(1)};
  float meas5[3] = {cc[5].at(0), cc[5].at(1)};
 
  // The update phase
  cv::Mat meas0Mat = cv::Mat(2, 1, CV_32F, meas0);
  cv::Mat meas1Mat = cv::Mat(2, 1, CV_32F, meas1);
  cv::Mat meas2Mat = cv::Mat(2, 1, CV_32F, meas2);
  cv::Mat meas3Mat = cv::Mat(2, 1, CV_32F, meas3);
  cv::Mat meas4Mat = cv::Mat(2, 1, CV_32F, meas4);
  cv::Mat meas5Mat = cv::Mat(2, 1, CV_32F, meas5);
 
  // cout<<"meas0Mat"<<meas0Mat<<"\n";
 
  Mat estimated0 = KF0.correct(meas0Mat);
  Mat estimated1 = KF0.correct(meas1Mat);
  Mat estimated2 = KF0.correct(meas2Mat);
  Mat estimated3 = KF0.correct(meas3Mat);
  Mat estimated4 = KF0.correct(meas4Mat);
  Mat estimated5 = KF0.correct(meas5Mat);
 
  // Publish the point clouds belonging to each clusters
 
  // cout<<"estimate="<<estimated.at<float>(0)<<","<<estimated.at<float>(1)<<"\n";
  // Point statePt(estimated.at<float>(0),estimated.at<float>(1));
  // cout<<"DONE KF_TRACKER\n";
}
void publish_cloud(ros::Publisher &pub,
                   pcl::PointCloud<pcl::PointXYZ>::Ptr cluster) {
  sensor_msgs::PointCloud2::Ptr clustermsg(new sensor_msgs::PointCloud2);
  pcl::toROSMsg(*cluster, *clustermsg);
  clustermsg->header.frame_id = "map";
  clustermsg->header.stamp = ros::Time::now();
  pub.publish(*clustermsg);
}
 
void cloud_cb(const sensor_msgs::PointCloud2ConstPtr &input)
 
{
  cout << "IF firstFrame=" << firstFrame << "\n";
  // If this is the first frame, initialize kalman filters for the clustered
  // objects
  if (firstFrame) {
    // Initialize 6 Kalman Filters; Assuming 6 max objects in the dataset.
    // Could be made generic by creating a Kalman Filter only when a new object
    // is detected
    KF0.transitionMatrix =
        *(Mat_<float>(4, 4) << 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1);
    KF1.transitionMatrix =
        *(Mat_<float>(4, 4) << 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1);
    KF2.transitionMatrix =
        *(Mat_<float>(4, 4) << 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1);
    KF3.transitionMatrix =
        *(Mat_<float>(4, 4) << 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1);
    KF4.transitionMatrix =
        *(Mat_<float>(4, 4) << 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1);
    KF5.transitionMatrix =
        *(Mat_<float>(4, 4) << 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1);
 
    cv::setIdentity(KF0.measurementMatrix);
    cv::setIdentity(KF1.measurementMatrix);
    cv::setIdentity(KF2.measurementMatrix);
    cv::setIdentity(KF3.measurementMatrix);
    cv::setIdentity(KF4.measurementMatrix);
    cv::setIdentity(KF5.measurementMatrix);
    // Process Noise Covariance Matrix Q
    // [ Ex 0  0    0 0    0 ]
    // [ 0  Ey 0    0 0    0 ]
    // [ 0  0  Ev_x 0 0    0 ]
    // [ 0  0  0    1 Ev_y 0 ]
    setIdentity(KF0.processNoiseCov, Scalar::all(1e-4));
    setIdentity(KF1.processNoiseCov, Scalar::all(1e-4));
    setIdentity(KF2.processNoiseCov, Scalar::all(1e-4));
    setIdentity(KF3.processNoiseCov, Scalar::all(1e-4));
    setIdentity(KF4.processNoiseCov, Scalar::all(1e-4));
    setIdentity(KF5.processNoiseCov, Scalar::all(1e-4));
    // Meas noise cov matrix R
    cv::setIdentity(KF0.measurementNoiseCov, cv::Scalar(1e-1));
    cv::setIdentity(KF1.measurementNoiseCov, cv::Scalar(1e-1));
    cv::setIdentity(KF2.measurementNoiseCov, cv::Scalar(1e-1));
    cv::setIdentity(KF3.measurementNoiseCov, cv::Scalar(1e-1));
    cv::setIdentity(KF4.measurementNoiseCov, cv::Scalar(1e-1));
    cv::setIdentity(KF5.measurementNoiseCov, cv::Scalar(1e-1));
 
    // Process the point cloud
    pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud(
        new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr clustered_cloud(
        new pcl::PointCloud<pcl::PointXYZ>);
    /* Creating the KdTree from input point cloud*/
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(
        new pcl::search::KdTree<pcl::PointXYZ>);
 
    pcl::fromROSMsg(*input, *input_cloud);
 
    tree->setInputCloud(input_cloud);
 
    /* Here we are creating a vector of PointIndices, which contains the actual
     * index information in a vector<int>. The indices of each detected cluster
     * are saved here. Cluster_indices is a vector containing one instance of
     * PointIndices for each detected cluster. Cluster_indices[0] contain all
     * indices of the first cluster in input point cloud.
     */
    std::vector<pcl::PointIndices> cluster_indices;
    pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
    ec.setClusterTolerance(0.08);
    ec.setMinClusterSize(10);
    ec.setMaxClusterSize(600);
    ec.setSearchMethod(tree);
    ec.setInputCloud(input_cloud);
    /* Extract the clusters out of pc and save indices in cluster_indices.*/
    ec.extract(cluster_indices);
 
    /* To separate each cluster out of the vector<PointIndices> we have to
     * iterate through cluster_indices, create a new PointCloud for each
     * entry and write all points of the current cluster in the PointCloud.
     */
    // pcl::PointXYZ origin (0,0,0);
    // float mindist_this_cluster = 1000;
    // float dist_this_point = 1000;
 
    std::vector<pcl::PointIndices>::const_iterator it;
    std::vector<int>::const_iterator pit;
    // Vector of cluster pointclouds
    std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> cluster_vec;
 
    for (it = cluster_indices.begin(); it != cluster_indices.end(); ++it) {
      pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster(
          new pcl::PointCloud<pcl::PointXYZ>);
      for (pit = it->indices.begin(); pit != it->indices.end(); pit++) {
 
        cloud_cluster->points.push_back(input_cloud->points[*pit]);
        // dist_this_point = pcl::geometry::distance(input_cloud->points[*pit],
        //                                          origin);
        // mindist_this_cluster = std::min(dist_this_point,
        // mindist_this_cluster);
      }
 
      cluster_vec.push_back(cloud_cluster);
    }
 
    // Ensure at least 6 clusters exist to publish (later clusters may be empty)
    while (cluster_vec.size() < 6) {
      pcl::PointCloud<pcl::PointXYZ>::Ptr empty_cluster(
          new pcl::PointCloud<pcl::PointXYZ>);
      empty_cluster->points.push_back(pcl::PointXYZ(0, 0, 0));
      cluster_vec.push_back(empty_cluster);
    }
 
    // Set initial state
    KF0.statePre.at<float>(0) =
        cluster_vec[0]->points[cluster_vec[0]->points.size() / 2].x;
 
    KF0.statePre.at<float>(1) =
        cluster_vec[0]->points[cluster_vec[0]->points.size() / 2].y;
    KF0.statePre.at<float>(2) = 0; // initial v_x
    KF0.statePre.at<float>(3) = 0; // initial v_y
 
    // Set initial state
    KF1.statePre.at<float>(0) =
        cluster_vec[1]->points[cluster_vec[1]->points.size() / 2].x;
    KF1.statePre.at<float>(1) =
        cluster_vec[1]->points[cluster_vec[1]->points.size() / 2].y;
    KF1.statePre.at<float>(2) = 0; // initial v_x
    KF1.statePre.at<float>(3) = 0; // initial v_y
 
    // Set initial state
    KF2.statePre.at<float>(0) =
        cluster_vec[2]->points[cluster_vec[2]->points.size() / 2].x;
    KF2.statePre.at<float>(1) =
        cluster_vec[2]->points[cluster_vec[2]->points.size() / 2].y;
    KF2.statePre.at<float>(2) = 0; // initial v_x
    KF2.statePre.at<float>(3) = 0; // initial v_y
 
    // Set initial state
    KF3.statePre.at<float>(0) =
        cluster_vec[3]->points[cluster_vec[3]->points.size() / 2].x;
    KF3.statePre.at<float>(1) =
        cluster_vec[3]->points[cluster_vec[3]->points.size() / 2].y;
    KF3.statePre.at<float>(2) = 0; // initial v_x
    KF3.statePre.at<float>(3) = 0; // initial v_y
 
    // Set initial state
    KF4.statePre.at<float>(0) =
        cluster_vec[4]->points[cluster_vec[4]->points.size() / 2].x;
    KF4.statePre.at<float>(1) =
        cluster_vec[4]->points[cluster_vec[4]->points.size() / 2].y;
    KF4.statePre.at<float>(2) = 0; // initial v_x
    KF4.statePre.at<float>(3) = 0; // initial v_y
 
    // Set initial state
    KF5.statePre.at<float>(0) =
        cluster_vec[5]->points[cluster_vec[5]->points.size() / 2].x;
    KF5.statePre.at<float>(1) =
        cluster_vec[5]->points[cluster_vec[5]->points.size() / 2].y;
    KF5.statePre.at<float>(2) = 0; // initial v_x
    KF5.statePre.at<float>(3) = 0; // initial v_y
 
    firstFrame = false;
 
    // Print the initial state of the kalman filter for debugging
    cout << "KF0.satePre=" << KF0.statePre.at<float>(0) << ","
         << KF0.statePre.at<float>(1) << "\n";
    cout << "KF1.satePre=" << KF1.statePre.at<float>(0) << ","
         << KF1.statePre.at<float>(1) << "\n";
    cout << "KF2.satePre=" << KF2.statePre.at<float>(0) << ","
         << KF2.statePre.at<float>(1) << "\n";
    cout << "KF3.satePre=" << KF3.statePre.at<float>(0) << ","
         << KF3.statePre.at<float>(1) << "\n";
    cout << "KF4.satePre=" << KF4.statePre.at<float>(0) << ","
         << KF4.statePre.at<float>(1) << "\n";
    cout << "KF5.satePre=" << KF5.statePre.at<float>(0) << ","
         << KF5.statePre.at<float>(1) << "\n";
 
    // cin.ignore();// To be able to see the printed initial state of the
    // KalmanFilter
 
    /* Naive version without kalman filter */
    for (int i = 0; i < 6; i++) {
      geometry_msgs::Point pt;
      pt.x = cluster_vec[i]->points[cluster_vec[i]->points.size() / 2].x;
      pt.y = cluster_vec[i]->points[cluster_vec[i]->points.size() / 2].y;
      prevClusterCenters.push_back(pt);
    }
 
    /* Naive version without kalman filter */
  }
 
  else {
    cout << "ELSE firstFrame=" << firstFrame << "\n";
    pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud(
        new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr clustered_cloud(
        new pcl::PointCloud<pcl::PointXYZ>);
    /* Creating the KdTree from input point cloud*/
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(
        new pcl::search::KdTree<pcl::PointXYZ>);
 
    pcl::fromROSMsg(*input, *input_cloud);
 
    tree->setInputCloud(input_cloud);
 
    /* Here we are creating a vector of PointIndices, which contains the actual
     * index information in a vector<int>. The indices of each detected cluster
     * are saved here. Cluster_indices is a vector containing one instance of
     * PointIndices for each detected cluster. Cluster_indices[0] contain all
     * indices of the first cluster in input point cloud.
     */
    std::vector<pcl::PointIndices> cluster_indices;
    pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
    ec.setClusterTolerance(0.08);
    ec.setMinClusterSize(10);
    ec.setMaxClusterSize(600);
    ec.setSearchMethod(tree);
    ec.setInputCloud(input_cloud);
    cout << "PCL init successfull\n";
    /* Extract the clusters out of pc and save indices in cluster_indices.*/
    ec.extract(cluster_indices);
    cout << "PCL extract successfull\n";
    /* To separate each cluster out of the vector<PointIndices> we have to
     * iterate through cluster_indices, create a new PointCloud for each
     * entry and write all points of the current cluster in the PointCloud.
     */
    // pcl::PointXYZ origin (0,0,0);
    // float mindist_this_cluster = 1000;
    // float dist_this_point = 1000;
 
    std::vector<pcl::PointIndices>::const_iterator it;
    std::vector<int>::const_iterator pit;
    // Vector of cluster pointclouds
    std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> cluster_vec;
 
    for (it = cluster_indices.begin(); it != cluster_indices.end(); ++it) {
      pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster(
          new pcl::PointCloud<pcl::PointXYZ>);
      for (pit = it->indices.begin(); pit != it->indices.end(); pit++) {
 
        cloud_cluster->points.push_back(input_cloud->points[*pit]);
        // dist_this_point = pcl::geometry::distance(input_cloud->points[*pit],
        //                                          origin);
        // mindist_this_cluster = std::min(dist_this_point,
        // mindist_this_cluster);
      }
 
      cluster_vec.push_back(cloud_cluster);
    }
    // cout<<"cluster_vec got some clusters\n";
 
    // Ensure at least 6 clusters exist to publish (later clusters may be empty)
    while (cluster_vec.size() < 6) {
      pcl::PointCloud<pcl::PointXYZ>::Ptr empty_cluster(
          new pcl::PointCloud<pcl::PointXYZ>);
      empty_cluster->points.push_back(pcl::PointXYZ(0, 0, 0));
      cluster_vec.push_back(empty_cluster);
    }
    std_msgs::Float32MultiArray cc;
    for (int i = 0; i < 6; i++) {
      cc.data.push_back(
          cluster_vec[i]->points[cluster_vec[i]->points.size() / 2].x);
      cc.data.push_back(
          cluster_vec[i]->points[cluster_vec[i]->points.size() / 2].y);
      cc.data.push_back(
          cluster_vec[i]->points[cluster_vec[i]->points.size() / 2].z);
    }
    cout << "6 clusters initialized\n";
 
    // cc_pos.publish(cc);// Publish cluster mid-points.
    KFT(cc);
    int i = 0;
    bool publishedCluster[6];
    for (auto it = objID.begin(); it != objID.end(); it++) {
      cout << "Inside the for loop\n";
 
      switch (*it) {
        cout << "Inside the switch case\n";
      case 0: {
        publish_cloud(pub_cluster0, cluster_vec[i]);
        publishedCluster[i] =
            true; // Use this flag to publish only once for a given obj ID
        i++;
        break;
      }
      case 1: {
        publish_cloud(pub_cluster1, cluster_vec[i]);
        publishedCluster[i] =
            true; // Use this flag to publish only once for a given obj ID
        i++;
        break;
      }
      case 2: {
        publish_cloud(pub_cluster2, cluster_vec[i]);
        publishedCluster[i] =
            true; // Use this flag to publish only once for a given obj ID
        i++;
        break;
      }
      case 3: {
        publish_cloud(pub_cluster3, cluster_vec[i]);
        publishedCluster[i] =
            true; // Use this flag to publish only once for a given obj ID
        i++;
        break;
      }
      case 4: {
        publish_cloud(pub_cluster4, cluster_vec[i]);
        publishedCluster[i] =
            true; // Use this flag to publish only once for a given obj ID
        i++;
        break;
      }
 
      case 5: {
        publish_cloud(pub_cluster5, cluster_vec[i]);
        publishedCluster[i] =
            true; // Use this flag to publish only once for a given obj ID
        i++;
        break;
      }
      default:
        break;
      }
    }
  }
}
 
int main(int argc, char **argv) {
  // ROS init
  ros::init(argc, argv, "KFTracker");
  ros::NodeHandle nh;
 
  // Publishers to publish the state of the objects (pos and vel)
  // objState1=nh.advertise<geometry_msgs::Twist> ("obj_1",1);
 
  cout << "About to setup callback\n";
 
  // Create a ROS subscriber for the input point cloud
  ros::Subscriber sub = nh.subscribe("filtered_cloud", 1, cloud_cb);
  // Create a ROS publisher for the output point cloud
  pub_cluster0 = nh.advertise<sensor_msgs::PointCloud2>("cluster_0", 1);
  pub_cluster1 = nh.advertise<sensor_msgs::PointCloud2>("cluster_1", 1);
  pub_cluster2 = nh.advertise<sensor_msgs::PointCloud2>("cluster_2", 1);
  pub_cluster3 = nh.advertise<sensor_msgs::PointCloud2>("cluster_3", 1);
  pub_cluster4 = nh.advertise<sensor_msgs::PointCloud2>("cluster_4", 1);
  pub_cluster5 = nh.advertise<sensor_msgs::PointCloud2>("cluster_5", 1);
  // Subscribe to the clustered pointclouds
  // ros::Subscriber c1=nh.subscribe("ccs",100,KFT);
  objID_pub = nh.advertise<std_msgs::Int32MultiArray>("obj_id", 1);
  /* Point cloud clustering
   */
 
  // cc_pos=nh.advertise<std_msgs::Float32MultiArray>("ccs",100);//clusterCenter1
 
  /* Point cloud clustering
   */
 
  ros::spin();
}