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Merge pull request #46 from praveen-palanisamy/rm-topic-slash-prefix 

Remove topic slash prefix
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Praveen Palanisamy 2021-08-29 10:41:25 -07:00 committed by GitHub
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@ -1,139 +1,136 @@
#include <iostream>
#include <string.h>
#include <fstream>
#include <algorithm>
#include <iterator>
#include "kf_tracker/featureDetection.h"
#include "kf_tracker/CKalmanFilter.h" #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/core/core.hpp>
#include <opencv2/highgui/highgui.hpp> #include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp> #include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/video/video.hpp> #include <opencv2/video/video.hpp>
#include "opencv2/video/tracking.hpp"
#include <ros/ros.h>
#include <pcl/io/pcd_io.h> #include <pcl/io/pcd_io.h>
#include <pcl/point_types.h> #include <pcl/point_types.h>
#include "pcl_ros/point_cloud.h" #include <ros/ros.h>
#include <geometry_msgs/Point.h>
#include <std_msgs/Float32MultiArray.h> #include <std_msgs/Float32MultiArray.h>
#include <std_msgs/Int32MultiArray.h> #include <std_msgs/Int32MultiArray.h>
#include <string.h>
#include <sensor_msgs/PointCloud2.h>
#include <pcl_conversions/pcl_conversions.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/common/geometry.h> #include <pcl/common/geometry.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/extract_indices.h> #include <pcl/filters/extract_indices.h>
#include <pcl/filters/voxel_grid.h> #include <pcl/filters/voxel_grid.h>
#include <pcl/features/normal_3d.h>
#include <pcl/kdtree/kdtree.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/method_types.h>
#include <pcl/sample_consensus/model_types.h> #include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/segmentation/extract_clusters.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 std;
using namespace cv; using namespace cv;
ros::Publisher objID_pub; ros::Publisher objID_pub;
// KF init // KF init
int stateDim=4;// [x,y,v_x,v_y]//,w,h] int stateDim = 4; // [x,y,v_x,v_y]//,w,h]
int measDim=2;// [z_x,z_y,z_w,z_h] int measDim = 2; // [z_x,z_y,z_w,z_h]
int ctrlDim=0; int ctrlDim = 0;
cv::KalmanFilter KF0(stateDim,measDim,ctrlDim,CV_32F); cv::KalmanFilter KF0(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF1(stateDim,measDim,ctrlDim,CV_32F); cv::KalmanFilter KF1(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF2(stateDim,measDim,ctrlDim,CV_32F); cv::KalmanFilter KF2(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF3(stateDim,measDim,ctrlDim,CV_32F); cv::KalmanFilter KF3(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF4(stateDim,measDim,ctrlDim,CV_32F); cv::KalmanFilter KF4(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF5(stateDim,measDim,ctrlDim,CV_32F); cv::KalmanFilter KF5(stateDim, measDim, ctrlDim, CV_32F);
ros::Publisher pub_cluster0; ros::Publisher pub_cluster0;
ros::Publisher pub_cluster1; ros::Publisher pub_cluster1;
ros::Publisher pub_cluster2; ros::Publisher pub_cluster2;
ros::Publisher pub_cluster3; ros::Publisher pub_cluster3;
ros::Publisher pub_cluster4; ros::Publisher pub_cluster4;
ros::Publisher pub_cluster5; ros::Publisher pub_cluster5;
std::vector<geometry_msgs::Point> prevClusterCenters; std::vector<geometry_msgs::Point> prevClusterCenters;
cv::Mat state(stateDim, 1, CV_32F);
cv::Mat_<float> measurement(2, 1);
cv::Mat state(stateDim,1,CV_32F); std::vector<int> objID; // Output of the data association using KF
cv::Mat_<float> measurement(2,1);
std::vector<int> objID;// Output of the data association using KF
// measurement.setTo(Scalar(0)); // measurement.setTo(Scalar(0));
bool firstFrame=true; bool firstFrame = true;
// calculate euclidean distance of two points // calculate euclidean distance of two points
double euclidean_distance(geometry_msgs::Point& p1, geometry_msgs::Point& p2) 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) +
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)); (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. //Count unique object IDs. just to make sure same ID has not been assigned to
int countIDs(vector<int> v) 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()) transform(v.begin(), v.end(), v.begin(), abs); // O(n) where n =
sort(v.begin(), v.end()); // Average case O(n log n), worst case O(n^2) (usually implemented as quicksort. distance(v.end(), v.begin()) sort(v.begin(), v.end()); // Average case O(n log
// To guarantee worst case O(n log n) replace with make_heap, then sort_heap. 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 // 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 // items to the back and returns an iterator to the end of the unique
auto unique_end = unique(v.begin(), v.end()); // Again n comparisons section of the range auto unique_end = unique(v.begin(), v.end()); // Again n
return distance(unique_end, v.begin()); // Constant time for random access iterators (like vector's) 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: vector containing the IDs of the clusters that should be associated with
objID[0] corresponds to KFT0, objID[1] corresponds to KFT1 etc. each KF_Tracker objID[0] corresponds to KFT0, objID[1] corresponds to KFT1 etc.
*/ */
void KFT(const std_msgs::Float32MultiArray ccs) void KFT(const std_msgs::Float32MultiArray ccs) {
{
// First predict, to update the internal statePre variable // 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()}; std::vector<cv::Mat> pred{KF0.predict(), KF1.predict(), KF2.predict(),
//cout<<"Pred successfull\n"; KF3.predict(), KF4.predict(), KF5.predict()};
// cout<<"Pred successfull\n";
//cv::Point predictPt(prediction.at<float>(0),prediction.at<float>(1)); // cv::Point predictPt(prediction.at<float>(0),prediction.at<float>(1));
// cout<<"Prediction 1 ="<<prediction.at<float>(0)<<","<<prediction.at<float>(1)<<"\n"; // cout<<"Prediction 1
// ="<<prediction.at<float>(0)<<","<<prediction.at<float>(1)<<"\n";
// Get measurements // Get measurements
// Extract the position of the clusters forom the multiArray. To check if the data // Extract the position of the clusters forom the multiArray. To check if the
// coming in, check the .z (every third) coordinate and that will be 0.0 // data coming in, check the .z (every third) coordinate and that will be 0.0
std::vector<geometry_msgs::Point> clusterCenters;//clusterCenters std::vector<geometry_msgs::Point> clusterCenters; // clusterCenters
int i=0; int i = 0;
for (std::vector<float>::const_iterator it=ccs.data.begin();it!=ccs.data.end();it+=3) for (std::vector<float>::const_iterator it = ccs.data.begin();
{ it != ccs.data.end(); it += 3) {
geometry_msgs::Point pt; geometry_msgs::Point pt;
pt.x=*it; pt.x = *it;
pt.y=*(it+1); pt.y = *(it + 1);
pt.z=*(it+2); pt.z = *(it + 2);
clusterCenters.push_back(pt); clusterCenters.push_back(pt);
} }
// cout<<"CLusterCenters Obtained"<<"\n"; // cout<<"CLusterCenters Obtained"<<"\n";
std::vector<geometry_msgs::Point> KFpredictions; std::vector<geometry_msgs::Point> KFpredictions;
i=0; i = 0;
for (auto it=pred.begin();it!=pred.end();it++) for (auto it = pred.begin(); it != pred.end(); it++) {
{
geometry_msgs::Point pt; geometry_msgs::Point pt;
pt.x=(*it).at<float>(0); pt.x = (*it).at<float>(0);
pt.y=(*it).at<float>(1); pt.y = (*it).at<float>(1);
pt.z=(*it).at<float>(2); pt.z = (*it).at<float>(2);
KFpredictions.push_back(pt); KFpredictions.push_back(pt);
} }
// cout<<"Got predictions"<<"\n"; // cout<<"Got predictions"<<"\n";
@ -147,9 +144,11 @@ void KFT(const std_msgs::Float32MultiArray ccs)
for(int n=0;n<6;n++) for(int n=0;n<6;n++)
distVec.push_back(euclidean_distance(KFpredictions[filterN],clusterCenters[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"; //
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()))); 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<<"MinD for
filter"<<filterN<<"="<<*min_element(distVec.begin(),distVec.end())<<"\n";
} }
@ -157,7 +156,7 @@ void KFT(const std_msgs::Float32MultiArray ccs)
//countIDs(objID);// for verif/corner cases //countIDs(objID);// for verif/corner cases
Original version using kalman filter prediction Original version using kalman filter prediction
*/ */
//display objIDs // display objIDs
/* DEBUG /* DEBUG
cout<<"objID= "; cout<<"objID= ";
for(auto it=objID.begin();it!=objID.end();it++) for(auto it=objID.begin();it!=objID.end();it++)
@ -165,41 +164,38 @@ void KFT(const std_msgs::Float32MultiArray ccs)
cout<<"\n"; cout<<"\n";
*/ */
/* Naive version without using kalman filter */ /* Naive version without using kalman filter */
objID.clear();//Clear the objID vector objID.clear(); // Clear the objID vector
for(int filterN=0;filterN<6;filterN++) for (int filterN = 0; filterN < 6; filterN++) {
{
std::vector<float> distVec; std::vector<float> distVec;
for(int n=0;n<6;n++) for (int n = 0; n < 6; n++)
distVec.push_back(euclidean_distance(prevClusterCenters[n],clusterCenters[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"; // 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()))); objID.push_back(std::distance(distVec.begin(),
// cout<<"MinD for filter"<<filterN<<"="<<*min_element(distVec.begin(),distVec.end())<<"\n"; min_element(distVec.begin(), distVec.end())));
// cout<<"MinD for
// filter"<<filterN<<"="<<*min_element(distVec.begin(),distVec.end())<<"\n";
} }
/* Naive version without kalman filter */ /* Naive version without kalman filter */
//prevClusterCenters.clear(); // prevClusterCenters.clear();
// Set the current associated clusters to the prevClusterCenters // Set the current associated clusters to the prevClusterCenters
for (int i=0;i<6;i++) for (int i = 0; i < 6; i++) {
{ prevClusterCenters[objID.at(i)] = clusterCenters.at(i);
prevClusterCenters[objID.at(i)]=clusterCenters.at(i);
} }
/* Naive version without kalman filter */ /* Naive version without kalman filter */
/* Naive version without using kalman filter */ /* Naive version without using kalman filter */
std_msgs::Int32MultiArray obj_id; std_msgs::Int32MultiArray obj_id;
for(auto it=objID.begin();it!=objID.end();it++) for (auto it = objID.begin(); it != objID.end(); it++)
obj_id.data.push_back(*it); obj_id.data.push_back(*it);
objID_pub.publish(obj_id); objID_pub.publish(obj_id);
// convert clusterCenters from geometry_msgs::Point to floats // convert clusterCenters from geometry_msgs::Point to floats
std::vector<std::vector<float> > cc; std::vector<std::vector<float>> cc;
for (int i=0;i<clusterCenters.size();i++) for (int i = 0; i < clusterCenters.size(); i++) {
{
vector<float> pt; vector<float> pt;
pt.push_back(clusterCenters[i].x); pt.push_back(clusterCenters[i].x);
pt.push_back(clusterCenters[i].y); pt.push_back(clusterCenters[i].y);
@ -207,25 +203,23 @@ objID.clear();//Clear the objID vector
cc.push_back(pt); 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"; // 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 meas0[3] = {cc[0].at(0), cc[0].at(1)};
float meas1[3]={cc[1].at(0),cc[1].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 meas2[3] = {cc[2].at(0), cc[2].at(1)};
float meas3[3]={cc[3].at(0),cc[3].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 meas4[3] = {cc[4].at(0), cc[4].at(1)};
float meas5[3]={cc[5].at(0),cc[5].at(1)}; float meas5[3] = {cc[5].at(0), cc[5].at(1)};
// The update phase // The update phase
cv::Mat meas0Mat=cv::Mat(2,1,CV_32F,meas0); cv::Mat meas0Mat = cv::Mat(2, 1, CV_32F, meas0);
cv::Mat meas1Mat=cv::Mat(2,1,CV_32F,meas1); cv::Mat meas1Mat = cv::Mat(2, 1, CV_32F, meas1);
cv::Mat meas2Mat=cv::Mat(2,1,CV_32F,meas2); cv::Mat meas2Mat = cv::Mat(2, 1, CV_32F, meas2);
cv::Mat meas3Mat=cv::Mat(2,1,CV_32F,meas3); cv::Mat meas3Mat = cv::Mat(2, 1, CV_32F, meas3);
cv::Mat meas4Mat=cv::Mat(2,1,CV_32F,meas4); cv::Mat meas4Mat = cv::Mat(2, 1, CV_32F, meas4);
cv::Mat meas5Mat=cv::Mat(2,1,CV_32F,meas5); cv::Mat meas5Mat = cv::Mat(2, 1, CV_32F, meas5);
//cout<<"meas0Mat"<<meas0Mat<<"\n"; // cout<<"meas0Mat"<<meas0Mat<<"\n";
Mat estimated0 = KF0.correct(meas0Mat); Mat estimated0 = KF0.correct(meas0Mat);
Mat estimated1 = KF0.correct(meas1Mat); Mat estimated1 = KF0.correct(meas1Mat);
@ -236,37 +230,41 @@ objID.clear();//Clear the objID vector
// Publish the point clouds belonging to each clusters // Publish the point clouds belonging to each clusters
// cout<<"estimate="<<estimated.at<float>(0)<<","<<estimated.at<float>(1)<<"\n"; // cout<<"estimate="<<estimated.at<float>(0)<<","<<estimated.at<float>(1)<<"\n";
// Point statePt(estimated.at<float>(0),estimated.at<float>(1)); // 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){ void publish_cloud(ros::Publisher &pub,
sensor_msgs::PointCloud2::Ptr clustermsg (new sensor_msgs::PointCloud2); pcl::PointCloud<pcl::PointXYZ>::Ptr cluster) {
pcl::toROSMsg (*cluster , *clustermsg); sensor_msgs::PointCloud2::Ptr clustermsg(new sensor_msgs::PointCloud2);
clustermsg->header.frame_id = "/map"; pcl::toROSMsg(*cluster, *clustermsg);
clustermsg->header.frame_id = "map";
clustermsg->header.stamp = ros::Time::now(); 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"; cout << "IF firstFrame=" << firstFrame << "\n";
// If this is the first frame, initialize kalman filters for the clustered objects // If this is the first frame, initialize kalman filters for the clustered
if (firstFrame) // objects
{ if (firstFrame) {
// Initialize 6 Kalman Filters; Assuming 6 max objects in the dataset. // 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 // Could be made generic by creating a Kalman Filter only when a new object
KF0.transitionMatrix = *(Mat_<float>(4, 4) << 1,0,1,0, 0,1,0,1, 0,0,1,0, 0,0,0,1); // is detected
KF1.transitionMatrix = *(Mat_<float>(4, 4) << 1,0,1,0, 0,1,0,1, 0,0,1,0, 0,0,0,1); KF0.transitionMatrix =
KF2.transitionMatrix = *(Mat_<float>(4, 4) << 1,0,1,0, 0,1,0,1, 0,0,1,0, 0,0,0,1); *(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); KF1.transitionMatrix =
KF4.transitionMatrix = *(Mat_<float>(4, 4) << 1,0,1,0, 0,1,0,1, 0,0,1,0, 0,0,0,1); *(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); 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(KF0.measurementMatrix);
cv::setIdentity(KF1.measurementMatrix); cv::setIdentity(KF1.measurementMatrix);
@ -295,309 +293,316 @@ if (firstFrame)
cv::setIdentity(KF4.measurementNoiseCov, cv::Scalar(1e-1)); cv::setIdentity(KF4.measurementNoiseCov, cv::Scalar(1e-1));
cv::setIdentity(KF5.measurementNoiseCov, cv::Scalar(1e-1)); cv::setIdentity(KF5.measurementNoiseCov, cv::Scalar(1e-1));
// Process the point cloud // Process the point cloud
pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud(
pcl::PointCloud<pcl::PointXYZ>::Ptr clustered_cloud (new pcl::PointCloud<pcl::PointXYZ>); new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr clustered_cloud(
new pcl::PointCloud<pcl::PointXYZ>);
/* Creating the KdTree from input point cloud*/ /* 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 /* Here we are creating a vector of PointIndices, which contains the actual
* information in a vector<int>. The indices of each detected cluster are saved here. * index information in a vector<int>. The indices of each detected cluster
* Cluster_indices is a vector containing one instance of PointIndices for each detected * are saved here. Cluster_indices is a vector containing one instance of
* cluster. Cluster_indices[0] contain all indices of the first cluster in input point cloud. * 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; std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec; pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (0.08); ec.setClusterTolerance(0.08);
ec.setMinClusterSize (10); ec.setMinClusterSize(10);
ec.setMaxClusterSize (600); ec.setMaxClusterSize(600);
ec.setSearchMethod (tree); ec.setSearchMethod(tree);
ec.setInputCloud (input_cloud); ec.setInputCloud(input_cloud);
/* Extract the clusters out of pc and save indices in cluster_indices.*/ /* 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 /* To separate each cluster out of the vector<PointIndices> we have to
* iterate through cluster_indices, create a new PointCloud for each * iterate through cluster_indices, create a new PointCloud for each
* entry and write all points of the current cluster in the PointCloud. * entry and write all points of the current cluster in the PointCloud.
*/ */
//pcl::PointXYZ origin (0,0,0); // pcl::PointXYZ origin (0,0,0);
//float mindist_this_cluster = 1000; // float mindist_this_cluster = 1000;
//float dist_this_point = 1000; // float dist_this_point = 1000;
std::vector<pcl::PointIndices>::const_iterator it; std::vector<pcl::PointIndices>::const_iterator it;
std::vector<int>::const_iterator pit; std::vector<int>::const_iterator pit;
// Vector of cluster pointclouds // 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) { for (it = cluster_indices.begin(); it != cluster_indices.end(); ++it) {
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster(
for(pit = it->indices.begin(); pit != it->indices.end(); pit++) new pcl::PointCloud<pcl::PointXYZ>);
{ for (pit = it->indices.begin(); pit != it->indices.end(); pit++) {
cloud_cluster->points.push_back(input_cloud->points[*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); // 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); cluster_vec.push_back(cloud_cluster);
} }
//Ensure at least 6 clusters exist to publish (later clusters may be empty) // Ensure at least 6 clusters exist to publish (later clusters may be empty)
while (cluster_vec.size() < 6){ while (cluster_vec.size() < 6) {
pcl::PointCloud<pcl::PointXYZ>::Ptr empty_cluster (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr empty_cluster(
empty_cluster->points.push_back(pcl::PointXYZ(0,0,0)); new pcl::PointCloud<pcl::PointXYZ>);
empty_cluster->points.push_back(pcl::PointXYZ(0, 0, 0));
cluster_vec.push_back(empty_cluster); 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 // Set initial state
KF0.statePre.at<float>(0)=cluster_vec[0]->points[cluster_vec[0]->points.size()/2].x; KF1.statePre.at<float>(0) =
cluster_vec[1]->points[cluster_vec[1]->points.size() / 2].x;
KF0.statePre.at<float>(1)=cluster_vec[0]->points[cluster_vec[0]->points.size()/2].y; KF1.statePre.at<float>(1) =
KF0.statePre.at<float>(2)=0;// initial v_x cluster_vec[1]->points[cluster_vec[1]->points.size() / 2].y;
KF0.statePre.at<float>(3)=0;//initial v_y KF1.statePre.at<float>(2) = 0; // initial v_x
KF1.statePre.at<float>(3) = 0; // initial v_y
// Set initial state // Set initial state
KF1.statePre.at<float>(0)=cluster_vec[1]->points[cluster_vec[1]->points.size()/2].x; KF2.statePre.at<float>(0) =
KF1.statePre.at<float>(1)=cluster_vec[1]->points[cluster_vec[1]->points.size()/2].y; cluster_vec[2]->points[cluster_vec[2]->points.size() / 2].x;
KF1.statePre.at<float>(2)=0;// initial v_x KF2.statePre.at<float>(1) =
KF1.statePre.at<float>(3)=0;//initial v_y 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 // Set initial state
KF2.statePre.at<float>(0)=cluster_vec[2]->points[cluster_vec[2]->points.size()/2].x; KF3.statePre.at<float>(0) =
KF2.statePre.at<float>(1)=cluster_vec[2]->points[cluster_vec[2]->points.size()/2].y; cluster_vec[3]->points[cluster_vec[3]->points.size() / 2].x;
KF2.statePre.at<float>(2)=0;// initial v_x KF3.statePre.at<float>(1) =
KF2.statePre.at<float>(3)=0;//initial v_y 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 // Set initial state
KF3.statePre.at<float>(0)=cluster_vec[3]->points[cluster_vec[3]->points.size()/2].x; KF4.statePre.at<float>(0) =
KF3.statePre.at<float>(1)=cluster_vec[3]->points[cluster_vec[3]->points.size()/2].y; cluster_vec[4]->points[cluster_vec[4]->points.size() / 2].x;
KF3.statePre.at<float>(2)=0;// initial v_x KF4.statePre.at<float>(1) =
KF3.statePre.at<float>(3)=0;//initial v_y 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 // Set initial state
KF4.statePre.at<float>(0)=cluster_vec[4]->points[cluster_vec[4]->points.size()/2].x; KF5.statePre.at<float>(0) =
KF4.statePre.at<float>(1)=cluster_vec[4]->points[cluster_vec[4]->points.size()/2].y; cluster_vec[5]->points[cluster_vec[5]->points.size() / 2].x;
KF4.statePre.at<float>(2)=0;// initial v_x KF5.statePre.at<float>(1) =
KF4.statePre.at<float>(3)=0;//initial v_y 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 firstFrame = false;
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 // 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 << "KF0.satePre=" << KF0.statePre.at<float>(0) << ","
cout<<"KF1.satePre="<<KF1.statePre.at<float>(0)<<","<<KF1.statePre.at<float>(1)<<"\n"; << KF0.statePre.at<float>(1) << "\n";
cout<<"KF2.satePre="<<KF2.statePre.at<float>(0)<<","<<KF2.statePre.at<float>(1)<<"\n"; cout << "KF1.satePre=" << KF1.statePre.at<float>(0) << ","
cout<<"KF3.satePre="<<KF3.statePre.at<float>(0)<<","<<KF3.statePre.at<float>(1)<<"\n"; << KF1.statePre.at<float>(1) << "\n";
cout<<"KF4.satePre="<<KF4.statePre.at<float>(0)<<","<<KF4.statePre.at<float>(1)<<"\n"; cout << "KF2.satePre=" << KF2.statePre.at<float>(0) << ","
cout<<"KF5.satePre="<<KF5.statePre.at<float>(0)<<","<<KF5.statePre.at<float>(1)<<"\n"; << KF2.statePre.at<float>(1) << "\n";
cout << "KF3.satePre=" << KF3.statePre.at<float>(0) << ","
//cin.ignore();// To be able to see the printed initial state of the KalmanFilter << 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 */ /* Naive version without kalman filter */
for (int i=0;i<6;i++) for (int i = 0; i < 6; i++) {
{
geometry_msgs::Point pt; geometry_msgs::Point pt;
pt.x=cluster_vec[i]->points[cluster_vec[i]->points.size()/2].x; 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.y = cluster_vec[i]->points[cluster_vec[i]->points.size() / 2].y;
prevClusterCenters.push_back(pt); prevClusterCenters.push_back(pt);
} }
/* Naive version without kalman filter */ /* Naive version without kalman filter */
} }
else {
else cout << "ELSE firstFrame=" << firstFrame << "\n";
{ pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud(
cout<<"ELSE firstFrame="<<firstFrame<<"\n"; new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr clustered_cloud(
pcl::PointCloud<pcl::PointXYZ>::Ptr clustered_cloud (new pcl::PointCloud<pcl::PointXYZ>); new pcl::PointCloud<pcl::PointXYZ>);
/* Creating the KdTree from input point cloud*/ /* 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 /* Here we are creating a vector of PointIndices, which contains the actual
* information in a vector<int>. The indices of each detected cluster are saved here. * index information in a vector<int>. The indices of each detected cluster
* Cluster_indices is a vector containing one instance of PointIndices for each detected * are saved here. Cluster_indices is a vector containing one instance of
* cluster. Cluster_indices[0] contain all indices of the first cluster in input point cloud. * 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; std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec; pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (0.08); ec.setClusterTolerance(0.08);
ec.setMinClusterSize (10); ec.setMinClusterSize(10);
ec.setMaxClusterSize (600); ec.setMaxClusterSize(600);
ec.setSearchMethod (tree); ec.setSearchMethod(tree);
ec.setInputCloud (input_cloud); ec.setInputCloud(input_cloud);
cout<<"PCL init successfull\n"; cout << "PCL init successfull\n";
/* Extract the clusters out of pc and save indices in cluster_indices.*/ /* Extract the clusters out of pc and save indices in cluster_indices.*/
ec.extract (cluster_indices); ec.extract(cluster_indices);
cout<<"PCL extract successfull\n"; cout << "PCL extract successfull\n";
/* To separate each cluster out of the vector<PointIndices> we have to /* To separate each cluster out of the vector<PointIndices> we have to
* iterate through cluster_indices, create a new PointCloud for each * iterate through cluster_indices, create a new PointCloud for each
* entry and write all points of the current cluster in the PointCloud. * entry and write all points of the current cluster in the PointCloud.
*/ */
//pcl::PointXYZ origin (0,0,0); // pcl::PointXYZ origin (0,0,0);
//float mindist_this_cluster = 1000; // float mindist_this_cluster = 1000;
//float dist_this_point = 1000; // float dist_this_point = 1000;
std::vector<pcl::PointIndices>::const_iterator it; std::vector<pcl::PointIndices>::const_iterator it;
std::vector<int>::const_iterator pit; std::vector<int>::const_iterator pit;
// Vector of cluster pointclouds // 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) { for (it = cluster_indices.begin(); it != cluster_indices.end(); ++it) {
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster(
for(pit = it->indices.begin(); pit != it->indices.end(); pit++) new pcl::PointCloud<pcl::PointXYZ>);
{ for (pit = it->indices.begin(); pit != it->indices.end(); pit++) {
cloud_cluster->points.push_back(input_cloud->points[*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); // 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); 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) // Ensure at least 6 clusters exist to publish (later clusters may be empty)
while (cluster_vec.size() < 6){ while (cluster_vec.size() < 6) {
pcl::PointCloud<pcl::PointXYZ>::Ptr empty_cluster (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr empty_cluster(
empty_cluster->points.push_back(pcl::PointXYZ(0,0,0)); new pcl::PointCloud<pcl::PointXYZ>);
empty_cluster->points.push_back(pcl::PointXYZ(0, 0, 0));
cluster_vec.push_back(empty_cluster); cluster_vec.push_back(empty_cluster);
} }
std_msgs::Float32MultiArray cc; std_msgs::Float32MultiArray cc;
for(int i=0;i<6;i++) for (int i = 0; i < 6; i++) {
{ cc.data.push_back(
cc.data.push_back(cluster_vec[i]->points[cluster_vec[i]->points.size()/2].x); 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(
cc.data.push_back(cluster_vec[i]->points[cluster_vec[i]->points.size()/2].z); 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); KFT(cc);
int i=0; int i = 0;
bool publishedCluster[6]; bool publishedCluster[6];
for(auto it=objID.begin();it!=objID.end();it++) for (auto it = objID.begin(); it != objID.end(); it++) {
{ cout<<"Inside the for loop\n"; cout << "Inside the for loop\n";
switch(*it) switch (*it) {
{ cout << "Inside the switch case\n";
cout<<"Inside the switch case\n";
case 0: { case 0: {
publish_cloud(pub_cluster0,cluster_vec[i]); publish_cloud(pub_cluster0, cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID publishedCluster[i] =
true; // Use this flag to publish only once for a given obj ID
i++; i++;
break; break;
} }
case 1: { case 1: {
publish_cloud(pub_cluster1,cluster_vec[i]); publish_cloud(pub_cluster1, cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID publishedCluster[i] =
true; // Use this flag to publish only once for a given obj ID
i++; i++;
break; break;
} }
case 2: { case 2: {
publish_cloud(pub_cluster2,cluster_vec[i]); publish_cloud(pub_cluster2, cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID publishedCluster[i] =
true; // Use this flag to publish only once for a given obj ID
i++; i++;
break; break;
} }
case 3: { case 3: {
publish_cloud(pub_cluster3,cluster_vec[i]); publish_cloud(pub_cluster3, cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID publishedCluster[i] =
true; // Use this flag to publish only once for a given obj ID
i++; i++;
break; break;
} }
case 4: { case 4: {
publish_cloud(pub_cluster4,cluster_vec[i]); publish_cloud(pub_cluster4, cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID publishedCluster[i] =
true; // Use this flag to publish only once for a given obj ID
i++; i++;
break; break;
} }
case 5: { case 5: {
publish_cloud(pub_cluster5,cluster_vec[i]); publish_cloud(pub_cluster5, cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID publishedCluster[i] =
true; // Use this flag to publish only once for a given obj ID
i++; i++;
break; break;
} }
default: break; default:
break;
}
} }
} }
} }
} int main(int argc, char **argv) {
int main(int argc, char** argv)
{
// ROS init // ROS init
ros::init (argc,argv,"KFTracker"); ros::init(argc, argv, "KFTracker");
ros::NodeHandle nh; ros::NodeHandle nh;
// Publishers to publish the state of the objects (pos and vel) // 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";
// Create a ROS subscriber for the input point cloud
cout<<"About to setup callback\n"; 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 // Create a ROS publisher for the output point cloud
pub_cluster0 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_0", 1); pub_cluster0 = nh.advertise<sensor_msgs::PointCloud2>("cluster_0", 1);
pub_cluster1 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_1", 1); pub_cluster1 = nh.advertise<sensor_msgs::PointCloud2>("cluster_1", 1);
pub_cluster2 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_2", 1); pub_cluster2 = nh.advertise<sensor_msgs::PointCloud2>("cluster_2", 1);
pub_cluster3 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_3", 1); pub_cluster3 = nh.advertise<sensor_msgs::PointCloud2>("cluster_3", 1);
pub_cluster4 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_4", 1); pub_cluster4 = nh.advertise<sensor_msgs::PointCloud2>("cluster_4", 1);
pub_cluster5 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_5", 1); pub_cluster5 = nh.advertise<sensor_msgs::PointCloud2>("cluster_5", 1);
// Subscribe to the clustered pointclouds // 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); 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 // cc_pos=nh.advertise<std_msgs::Float32MultiArray>("ccs",100);//clusterCenter1
/* Point cloud clustering
*/
/* Point cloud clustering
*/
ros::spin(); ros::spin();
} }