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@ -1,139 +1,136 @@
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#include <iostream>
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#include <string.h>
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#include <fstream>
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#include <algorithm>
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#include <iterator>
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#include "kf_tracker/featureDetection.h"
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#include "kf_tracker/CKalmanFilter.h"
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#include "kf_tracker/featureDetection.h"
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#include "opencv2/video/tracking.hpp"
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#include "pcl_ros/point_cloud.h"
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#include <algorithm>
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#include <fstream>
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#include <geometry_msgs/Point.h>
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#include <iostream>
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#include <iterator>
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include <opencv2/video/video.hpp>
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#include "opencv2/video/tracking.hpp"
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#include <ros/ros.h>
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#include <pcl/io/pcd_io.h>
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#include <pcl/point_types.h>
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#include "pcl_ros/point_cloud.h"
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#include <geometry_msgs/Point.h>
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#include <ros/ros.h>
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#include <std_msgs/Float32MultiArray.h>
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#include <std_msgs/Int32MultiArray.h>
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#include <string.h>
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#include <sensor_msgs/PointCloud2.h>
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#include <pcl_conversions/pcl_conversions.h>
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#include <pcl/point_cloud.h>
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#include <pcl/point_types.h>
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#include <pcl/common/geometry.h>
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#include <pcl/features/normal_3d.h>
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#include <pcl/filters/extract_indices.h>
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#include <pcl/filters/voxel_grid.h>
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#include <pcl/features/normal_3d.h>
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#include <pcl/kdtree/kdtree.h>
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#include <pcl/point_cloud.h>
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#include <pcl/point_types.h>
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#include <pcl/sample_consensus/method_types.h>
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#include <pcl/sample_consensus/model_types.h>
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#include <pcl/segmentation/sac_segmentation.h>
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#include <pcl/segmentation/extract_clusters.h>
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#include <pcl/segmentation/sac_segmentation.h>
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#include <pcl_conversions/pcl_conversions.h>
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#include <sensor_msgs/PointCloud2.h>
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using namespace std;
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using namespace cv;
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ros::Publisher objID_pub;
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// KF init
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int stateDim=4;// [x,y,v_x,v_y]//,w,h]
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int measDim=2;// [z_x,z_y,z_w,z_h]
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int ctrlDim=0;
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cv::KalmanFilter KF0(stateDim,measDim,ctrlDim,CV_32F);
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cv::KalmanFilter KF1(stateDim,measDim,ctrlDim,CV_32F);
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cv::KalmanFilter KF2(stateDim,measDim,ctrlDim,CV_32F);
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cv::KalmanFilter KF3(stateDim,measDim,ctrlDim,CV_32F);
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cv::KalmanFilter KF4(stateDim,measDim,ctrlDim,CV_32F);
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cv::KalmanFilter KF5(stateDim,measDim,ctrlDim,CV_32F);
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// KF init
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int stateDim = 4; // [x,y,v_x,v_y]//,w,h]
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int measDim = 2; // [z_x,z_y,z_w,z_h]
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int ctrlDim = 0;
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cv::KalmanFilter KF0(stateDim, measDim, ctrlDim, CV_32F);
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cv::KalmanFilter KF1(stateDim, measDim, ctrlDim, CV_32F);
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cv::KalmanFilter KF2(stateDim, measDim, ctrlDim, CV_32F);
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cv::KalmanFilter KF3(stateDim, measDim, ctrlDim, CV_32F);
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cv::KalmanFilter KF4(stateDim, measDim, ctrlDim, CV_32F);
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cv::KalmanFilter KF5(stateDim, measDim, ctrlDim, CV_32F);
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ros::Publisher pub_cluster0;
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ros::Publisher pub_cluster1;
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ros::Publisher pub_cluster2;
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ros::Publisher pub_cluster3;
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ros::Publisher pub_cluster4;
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ros::Publisher pub_cluster5;
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ros::Publisher pub_cluster0;
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ros::Publisher pub_cluster1;
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ros::Publisher pub_cluster2;
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ros::Publisher pub_cluster3;
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ros::Publisher pub_cluster4;
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ros::Publisher pub_cluster5;
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std::vector<geometry_msgs::Point> prevClusterCenters;
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std::vector<geometry_msgs::Point> prevClusterCenters;
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cv::Mat state(stateDim, 1, CV_32F);
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cv::Mat_<float> measurement(2, 1);
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cv::Mat state(stateDim,1,CV_32F);
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cv::Mat_<float> measurement(2,1);
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std::vector<int> objID;// Output of the data association using KF
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std::vector<int> objID; // Output of the data association using KF
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// measurement.setTo(Scalar(0));
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bool firstFrame=true;
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bool firstFrame = true;
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// calculate euclidean distance of two points
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double euclidean_distance(geometry_msgs::Point& p1, geometry_msgs::Point& p2)
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{
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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));
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}
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double euclidean_distance(geometry_msgs::Point &p1, geometry_msgs::Point &p2) {
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return sqrt((p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y - p2.y) +
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(p1.z - p2.z) * (p1.z - p2.z));
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}
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/*
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//Count unique object IDs. just to make sure same ID has not been assigned to two KF_Trackers.
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int countIDs(vector<int> v)
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//Count unique object IDs. just to make sure same ID has not been assigned to
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two KF_Trackers. int countIDs(vector<int> v)
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{
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transform(v.begin(), v.end(), v.begin(), abs); // O(n) where n = distance(v.end(), v.begin())
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sort(v.begin(), v.end()); // Average case O(n log n), worst case O(n^2) (usually implemented as quicksort.
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// To guarantee worst case O(n log n) replace with make_heap, then sort_heap.
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transform(v.begin(), v.end(), v.begin(), abs); // O(n) where n =
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distance(v.end(), v.begin()) sort(v.begin(), v.end()); // Average case O(n log
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n), worst case O(n^2) (usually implemented as quicksort.
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// To guarantee worst case O(n log n) replace with make_heap, then
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sort_heap.
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// Unique will take a sorted range, and move things around to get duplicated
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// items to the back and returns an iterator to the end of the unique section of the range
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auto unique_end = unique(v.begin(), v.end()); // Again n comparisons
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return distance(unique_end, v.begin()); // Constant time for random access iterators (like vector's)
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// items to the back and returns an iterator to the end of the unique
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section of the range auto unique_end = unique(v.begin(), v.end()); // Again n
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comparisons return distance(unique_end, v.begin()); // Constant time for random
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access iterators (like vector's)
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}
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*/
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/*
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objID: vector containing the IDs of the clusters that should be associated with each KF_Tracker
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objID[0] corresponds to KFT0, objID[1] corresponds to KFT1 etc.
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objID: vector containing the IDs of the clusters that should be associated with
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each KF_Tracker objID[0] corresponds to KFT0, objID[1] corresponds to KFT1 etc.
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*/
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void KFT(const std_msgs::Float32MultiArray ccs)
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{
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void KFT(const std_msgs::Float32MultiArray ccs) {
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// First predict, to update the internal statePre variable
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std::vector<cv::Mat> pred{KF0.predict(),KF1.predict(),KF2.predict(),KF3.predict(),KF4.predict(),KF5.predict()};
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//cout<<"Pred successfull\n";
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std::vector<cv::Mat> pred{KF0.predict(), KF1.predict(), KF2.predict(),
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KF3.predict(), KF4.predict(), KF5.predict()};
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// cout<<"Pred successfull\n";
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//cv::Point predictPt(prediction.at<float>(0),prediction.at<float>(1));
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// cout<<"Prediction 1 ="<<prediction.at<float>(0)<<","<<prediction.at<float>(1)<<"\n";
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// cv::Point predictPt(prediction.at<float>(0),prediction.at<float>(1));
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// cout<<"Prediction 1
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// ="<<prediction.at<float>(0)<<","<<prediction.at<float>(1)<<"\n";
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// Get measurements
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// Extract the position of the clusters forom the multiArray. To check if the data
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// coming in, check the .z (every third) coordinate and that will be 0.0
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std::vector<geometry_msgs::Point> clusterCenters;//clusterCenters
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// Extract the position of the clusters forom the multiArray. To check if the
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// data coming in, check the .z (every third) coordinate and that will be 0.0
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std::vector<geometry_msgs::Point> clusterCenters; // clusterCenters
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int i=0;
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for (std::vector<float>::const_iterator it=ccs.data.begin();it!=ccs.data.end();it+=3)
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{
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int i = 0;
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for (std::vector<float>::const_iterator it = ccs.data.begin();
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it != ccs.data.end(); it += 3) {
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geometry_msgs::Point pt;
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pt.x=*it;
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pt.y=*(it+1);
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pt.z=*(it+2);
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pt.x = *it;
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pt.y = *(it + 1);
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pt.z = *(it + 2);
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clusterCenters.push_back(pt);
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}
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// cout<<"CLusterCenters Obtained"<<"\n";
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std::vector<geometry_msgs::Point> KFpredictions;
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i=0;
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for (auto it=pred.begin();it!=pred.end();it++)
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{
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i = 0;
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for (auto it = pred.begin(); it != pred.end(); it++) {
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geometry_msgs::Point pt;
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pt.x=(*it).at<float>(0);
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pt.y=(*it).at<float>(1);
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pt.z=(*it).at<float>(2);
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pt.x = (*it).at<float>(0);
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pt.y = (*it).at<float>(1);
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pt.z = (*it).at<float>(2);
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KFpredictions.push_back(pt);
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}
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// cout<<"Got predictions"<<"\n";
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@ -147,9 +144,11 @@ void KFT(const std_msgs::Float32MultiArray ccs)
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for(int n=0;n<6;n++)
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distVec.push_back(euclidean_distance(KFpredictions[filterN],clusterCenters[n]));
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// cout<<"distVec[="<<distVec[0]<<","<<distVec[1]<<","<<distVec[2]<<","<<distVec[3]<<","<<distVec[4]<<","<<distVec[5]<<"\n";
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//
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cout<<"distVec[="<<distVec[0]<<","<<distVec[1]<<","<<distVec[2]<<","<<distVec[3]<<","<<distVec[4]<<","<<distVec[5]<<"\n";
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objID.push_back(std::distance(distVec.begin(),min_element(distVec.begin(),distVec.end())));
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// cout<<"MinD for filter"<<filterN<<"="<<*min_element(distVec.begin(),distVec.end())<<"\n";
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// cout<<"MinD for
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filter"<<filterN<<"="<<*min_element(distVec.begin(),distVec.end())<<"\n";
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}
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@ -157,7 +156,7 @@ void KFT(const std_msgs::Float32MultiArray ccs)
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//countIDs(objID);// for verif/corner cases
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Original version using kalman filter prediction
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*/
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//display objIDs
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// display objIDs
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/* DEBUG
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cout<<"objID= ";
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for(auto it=objID.begin();it!=objID.end();it++)
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@ -165,41 +164,38 @@ void KFT(const std_msgs::Float32MultiArray ccs)
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cout<<"\n";
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*/
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/* Naive version without using kalman filter */
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objID.clear();//Clear the objID vector
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for(int filterN=0;filterN<6;filterN++)
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{
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/* Naive version without using kalman filter */
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objID.clear(); // Clear the objID vector
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for (int filterN = 0; filterN < 6; filterN++) {
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std::vector<float> distVec;
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for(int n=0;n<6;n++)
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distVec.push_back(euclidean_distance(prevClusterCenters[n],clusterCenters[n]));
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for (int n = 0; n < 6; n++)
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distVec.push_back(
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euclidean_distance(prevClusterCenters[n], clusterCenters[n]));
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// cout<<"distVec[="<<distVec[0]<<","<<distVec[1]<<","<<distVec[2]<<","<<distVec[3]<<","<<distVec[4]<<","<<distVec[5]<<"\n";
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objID.push_back(std::distance(distVec.begin(),min_element(distVec.begin(),distVec.end())));
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// cout<<"MinD for filter"<<filterN<<"="<<*min_element(distVec.begin(),distVec.end())<<"\n";
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objID.push_back(std::distance(distVec.begin(),
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min_element(distVec.begin(), distVec.end())));
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// cout<<"MinD for
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// filter"<<filterN<<"="<<*min_element(distVec.begin(),distVec.end())<<"\n";
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}
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/* Naive version without kalman filter */
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//prevClusterCenters.clear();
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// prevClusterCenters.clear();
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// Set the current associated clusters to the prevClusterCenters
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for (int i=0;i<6;i++)
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{
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prevClusterCenters[objID.at(i)]=clusterCenters.at(i);
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for (int i = 0; i < 6; i++) {
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prevClusterCenters[objID.at(i)] = clusterCenters.at(i);
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}
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|
|
|
|
|
/* Naive version without kalman filter */
|
|
|
|
|
|
|
|
|
|
/* Naive version without using kalman filter */
|
|
|
|
|
|
|
|
|
|
/* Naive version without using kalman filter */
|
|
|
|
|
|
|
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|
|
std_msgs::Int32MultiArray obj_id;
|
|
|
|
|
for(auto it=objID.begin();it!=objID.end();it++)
|
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|
|
|
for (auto it = objID.begin(); it != objID.end(); it++)
|
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|
|
obj_id.data.push_back(*it);
|
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|
|
objID_pub.publish(obj_id);
|
|
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|
|
// convert clusterCenters from geometry_msgs::Point to floats
|
|
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|
|
std::vector<std::vector<float> > cc;
|
|
|
|
|
for (int i=0;i<clusterCenters.size();i++)
|
|
|
|
|
{
|
|
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|
|
std::vector<std::vector<float>> cc;
|
|
|
|
|
for (int i = 0; i < clusterCenters.size(); i++) {
|
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|
|
vector<float> pt;
|
|
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|
|
pt.push_back(clusterCenters[i].x);
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|
|
pt.push_back(clusterCenters[i].y);
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|
@ -207,25 +203,23 @@ objID.clear();//Clear the objID vector
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cc.push_back(pt);
|
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|
}
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|
//cout<<"cc[5][0]="<<cc[5].at(0)<<"cc[5][1]="<<cc[5].at(1)<<"cc[5][2]="<<cc[5].at(2)<<"\n";
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|
|
float meas0[3]={cc[0].at(0),cc[0].at(1)};
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|
|
float meas1[3]={cc[1].at(0),cc[1].at(1)};
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float meas2[3]={cc[2].at(0),cc[2].at(1)};
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float meas3[3]={cc[3].at(0),cc[3].at(1)};
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|
float meas4[3]={cc[4].at(0),cc[4].at(1)};
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float meas5[3]={cc[5].at(0),cc[5].at(1)};
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// cout<<"cc[5][0]="<<cc[5].at(0)<<"cc[5][1]="<<cc[5].at(1)<<"cc[5][2]="<<cc[5].at(2)<<"\n";
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|
|
float meas0[3] = {cc[0].at(0), cc[0].at(1)};
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|
|
float meas1[3] = {cc[1].at(0), cc[1].at(1)};
|
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|
float meas2[3] = {cc[2].at(0), cc[2].at(1)};
|
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|
|
float meas3[3] = {cc[3].at(0), cc[3].at(1)};
|
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|
float meas4[3] = {cc[4].at(0), cc[4].at(1)};
|
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|
float meas5[3] = {cc[5].at(0), cc[5].at(1)};
|
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|
|
// The update phase
|
|
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|
cv::Mat meas0Mat=cv::Mat(2,1,CV_32F,meas0);
|
|
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|
|
cv::Mat meas1Mat=cv::Mat(2,1,CV_32F,meas1);
|
|
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|
|
cv::Mat meas2Mat=cv::Mat(2,1,CV_32F,meas2);
|
|
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|
|
cv::Mat meas3Mat=cv::Mat(2,1,CV_32F,meas3);
|
|
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|
|
cv::Mat meas4Mat=cv::Mat(2,1,CV_32F,meas4);
|
|
|
|
|
cv::Mat meas5Mat=cv::Mat(2,1,CV_32F,meas5);
|
|
|
|
|
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";
|
|
|
|
|
// cout<<"meas0Mat"<<meas0Mat<<"\n";
|
|
|
|
|
|
|
|
|
|
Mat estimated0 = KF0.correct(meas0Mat);
|
|
|
|
|
Mat estimated1 = KF0.correct(meas1Mat);
|
|
|
|
|
@ -236,37 +230,41 @@ objID.clear();//Clear the objID vector
|
|
|
|
|
|
|
|
|
|
// 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";
|
|
|
|
|
|
|
|
|
|
// 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";
|
|
|
|
|
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);
|
|
|
|
|
|
|
|
|
|
pub.publish(*clustermsg);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
void cloud_cb (const sensor_msgs::PointCloud2ConstPtr& input)
|
|
|
|
|
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)
|
|
|
|
|
{
|
|
|
|
|
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);
|
|
|
|
|
// 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);
|
|
|
|
|
@ -295,309 +293,316 @@ if (firstFrame)
|
|
|
|
|
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>);
|
|
|
|
|
// 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::search::KdTree<pcl::PointXYZ>::Ptr tree(
|
|
|
|
|
new pcl::search::KdTree<pcl::PointXYZ>);
|
|
|
|
|
|
|
|
|
|
pcl::fromROSMsg (*input, *input_cloud);
|
|
|
|
|
pcl::fromROSMsg(*input, *input_cloud);
|
|
|
|
|
|
|
|
|
|
tree->setInputCloud (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.
|
|
|
|
|
/* 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);
|
|
|
|
|
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);
|
|
|
|
|
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;
|
|
|
|
|
// 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;
|
|
|
|
|
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++)
|
|
|
|
|
{
|
|
|
|
|
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],
|
|
|
|
|
// dist_this_point = pcl::geometry::distance(input_cloud->points[*pit],
|
|
|
|
|
// origin);
|
|
|
|
|
//mindist_this_cluster = std::min(dist_this_point, mindist_this_cluster);
|
|
|
|
|
// 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));
|
|
|
|
|
// 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
|
|
|
|
|
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
|
|
|
|
|
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
|
|
|
|
|
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
|
|
|
|
|
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
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
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
|
|
|
|
|
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
|
|
|
|
|
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
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
// 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;
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
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++)
|
|
|
|
|
{
|
|
|
|
|
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;
|
|
|
|
|
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>);
|
|
|
|
|
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::search::KdTree<pcl::PointXYZ>::Ptr tree(
|
|
|
|
|
new pcl::search::KdTree<pcl::PointXYZ>);
|
|
|
|
|
|
|
|
|
|
pcl::fromROSMsg (*input, *input_cloud);
|
|
|
|
|
pcl::fromROSMsg(*input, *input_cloud);
|
|
|
|
|
|
|
|
|
|
tree->setInputCloud (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.
|
|
|
|
|
/* 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";
|
|
|
|
|
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";
|
|
|
|
|
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;
|
|
|
|
|
// 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;
|
|
|
|
|
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++)
|
|
|
|
|
{
|
|
|
|
|
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],
|
|
|
|
|
// dist_this_point = pcl::geometry::distance(input_cloud->points[*pit],
|
|
|
|
|
// origin);
|
|
|
|
|
//mindist_this_cluster = std::min(dist_this_point, mindist_this_cluster);
|
|
|
|
|
// mindist_this_cluster = std::min(dist_this_point,
|
|
|
|
|
// mindist_this_cluster);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
cluster_vec.push_back(cloud_cluster);
|
|
|
|
|
|
|
|
|
|
}
|
|
|
|
|
//cout<<"cluster_vec got some clusters\n";
|
|
|
|
|
// 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));
|
|
|
|
|
// 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);
|
|
|
|
|
|
|
|
|
|
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";
|
|
|
|
|
cout << "6 clusters initialized\n";
|
|
|
|
|
|
|
|
|
|
//cc_pos.publish(cc);// Publish cluster mid-points.
|
|
|
|
|
// cc_pos.publish(cc);// Publish cluster mid-points.
|
|
|
|
|
KFT(cc);
|
|
|
|
|
int i=0;
|
|
|
|
|
int i = 0;
|
|
|
|
|
bool publishedCluster[6];
|
|
|
|
|
for(auto it=objID.begin();it!=objID.end();it++)
|
|
|
|
|
{ cout<<"Inside the for loop\n";
|
|
|
|
|
for (auto it = objID.begin(); it != objID.end(); it++) {
|
|
|
|
|
cout << "Inside the for loop\n";
|
|
|
|
|
|
|
|
|
|
switch(*it)
|
|
|
|
|
{
|
|
|
|
|
cout<<"Inside the switch case\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
|
|
|
|
|
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
|
|
|
|
|
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
|
|
|
|
|
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
|
|
|
|
|
publish_cloud(pub_cluster3, cluster_vec[i]);
|
|
|
|
|
publishedCluster[i] =
|
|
|
|
|
true; // Use this flag to publish only once for a given obj ID
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i++;
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break;
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}
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case 4: {
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publish_cloud(pub_cluster4,cluster_vec[i]);
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publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID
|
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publish_cloud(pub_cluster4, cluster_vec[i]);
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publishedCluster[i] =
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true; // Use this flag to publish only once for a given obj ID
|
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i++;
|
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|
break;
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}
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case 5: {
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publish_cloud(pub_cluster5,cluster_vec[i]);
|
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|
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publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID
|
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|
publish_cloud(pub_cluster5, cluster_vec[i]);
|
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|
|
|
publishedCluster[i] =
|
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|
true; // Use this flag to publish only once for a given obj ID
|
|
|
|
|
i++;
|
|
|
|
|
break;
|
|
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|
|
|
|
|
|
}
|
|
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|
default: break;
|
|
|
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|
default:
|
|
|
|
|
break;
|
|
|
|
|
}
|
|
|
|
|
}
|
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|
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|
|
|
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}
|
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}
|
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|
|
|
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|
|
|
}
|
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|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
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|
|
int main(int argc, char** argv)
|
|
|
|
|
{
|
|
|
|
|
int main(int argc, char **argv) {
|
|
|
|
|
// ROS init
|
|
|
|
|
ros::init (argc,argv,"KFTracker");
|
|
|
|
|
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);
|
|
|
|
|
// objState1=nh.advertise<geometry_msgs::Twist> ("obj_1",1);
|
|
|
|
|
|
|
|
|
|
cout << "About to setup callback\n";
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 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);
|
|
|
|
|
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);
|
|
|
|
|
// ros::Subscriber c1=nh.subscribe("ccs",100,KFT);
|
|
|
|
|
objID_pub = nh.advertise<std_msgs::Int32MultiArray>("obj_id", 1);
|
|
|
|
|
/* Point cloud clustering
|
|
|
|
|
*/
|
|
|
|
|
/* Point cloud clustering
|
|
|
|
|
*/
|
|
|
|
|
|
|
|
|
|
//cc_pos=nh.advertise<std_msgs::Float32MultiArray>("ccs",100);//clusterCenter1
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
/* Point cloud clustering
|
|
|
|
|
*/
|
|
|
|
|
// cc_pos=nh.advertise<std_msgs::Float32MultiArray>("ccs",100);//clusterCenter1
|
|
|
|
|
|
|
|
|
|
/* Point cloud clustering
|
|
|
|
|
*/
|
|
|
|
|
|
|
|
|
|
ros::spin();
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
}
|
|
|
|
|
|
Praveen Palanisamy
Praveen Palanisamy
Artur Sagitov