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v2. Unsupervised clustering is incorporated into the same node (tracker).

master
Praveen Palanisamy 2015-12-07 21:05:32 -05:00
parent 5d4e3c8d08
commit 4c569c598e
1 changed files with 308 additions and 36 deletions
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340
src/main.cpp
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@ -18,9 +18,24 @@
#include <std_msgs/Float32MultiArray.h>
#include <std_msgs/Int32MultiArray.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/filters/extract_indices.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/features/normal_3d.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/segmentation/extract_clusters.h>
using namespace std;
using namespace cv;
ros::Publisher objID_pub;
// KF init
@ -34,11 +49,21 @@ ros::Publisher objID_pub;
cv::KalmanFilter KF4(stateDim,measDim,ctrlDim,CV_32F);
cv::KalmanFilter KF5(stateDim,measDim,ctrlDim,CV_32F);
ros::Publisher pub_cluster0;
ros::Publisher pub_cluster1;
ros::Publisher pub_cluster2;
ros::Publisher pub_cluster3;
ros::Publisher pub_cluster4;
ros::Publisher pub_cluster5;
cv::Mat state(stateDim,1,CV_32F);
cv::Mat_<float> measurement(2,1);
std::vector<int> objID;// Output of the data association using KF
// measurement.setTo(Scalar(0));
bool firstFrame=true;
// calculate euclidean distance of two points
double euclidean_distance(geometry_msgs::Point& p1, geometry_msgs::Point& p2)
@ -65,19 +90,12 @@ int countIDs(vector<int> v)
objID: vector containing the IDs of the clusters that should be associated with each KF_Tracker
objID[0] corresponds to KFT0, objID[1] corresponds to KFT1 etc.
*/
void KFT(const std_msgs::Float32MultiArray::ConstPtr& ccs)
void KFT(const std_msgs::Float32MultiArray ccs)
{
// First predict, to update the internal statePre variable
/* Mat prediction0 = KF0.predict();
Mat prediction1 = KF1.predict();
Mat prediction2 = KF2.predict();
Mat prediction3 = KF3.predict();
Mat prediction4 = KF4.predict();
Mat prediction5 = KF5.predict();
*/
std::vector<cv::Mat> pred{KF0.predict(),KF1.predict(),KF2.predict(),KF3.predict(),KF4.predict(),KF5.predict()};
//cout<<"Pred successfull\n";
@ -91,7 +109,7 @@ void KFT(const std_msgs::Float32MultiArray::ConstPtr& ccs)
std::vector<geometry_msgs::Point> clusterCenters;//clusterCenters
int i=0;
for (std::vector<float>::const_iterator it=ccs->data.begin();it!=ccs->data.end();it+=3)
for (std::vector<float>::const_iterator it=ccs.data.begin();it!=ccs.data.end();it+=3)
{
geometry_msgs::Point pt;
pt.x=*it;
@ -117,10 +135,10 @@ void KFT(const std_msgs::Float32MultiArray::ConstPtr& ccs)
}
// cout<<"Got predictions"<<"\n";
std::vector<int> objID;
// Find the cluster that is more probable to be belonging to a given KF.
objID.clear();//Clear the objID vector
for(int filterN=0;filterN<6;filterN++)
{
std::vector<float> distVec;
@ -184,6 +202,7 @@ void KFT(const std_msgs::Float32MultiArray::ConstPtr& ccs)
Mat estimated4 = KF0.correct(meas4Mat);
Mat estimated5 = KF0.correct(meas5Mat);
// Publish the point clouds belonging to each clusters
// cout<<"estimate="<<estimated.at<float>(0)<<","<<estimated.at<float>(1)<<"\n";
@ -191,18 +210,25 @@ void KFT(const std_msgs::Float32MultiArray::ConstPtr& ccs)
//cout<<"DONE KF_TRACKER\n";
}
void publish_cloud(ros::Publisher& pub, pcl::PointCloud<pcl::PointXYZ>::Ptr cluster){
sensor_msgs::PointCloud2::Ptr clustermsg (new sensor_msgs::PointCloud2);
pcl::toROSMsg (*cluster , *clustermsg);
clustermsg->header.frame_id = "/map";
clustermsg->header.stamp = ros::Time::now();
pub.publish (*clustermsg);
}
int main(int argc, char** argv)
void cloud_cb (const sensor_msgs::PointCloud2ConstPtr& input)
{
// ROS init
ros::init (argc,argv,"KFTracker");
ros::NodeHandle nh;
// Publishers to publish the state of the objects (pos and vel)
//objState1=nh.advertise<geometry_msgs::Twist> ("obj_1",1);
cout<<"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);
@ -237,47 +263,293 @@ int main(int argc, char** argv)
cv::setIdentity(KF4.measurementNoiseCov, cv::Scalar(1e-1));
cv::setIdentity(KF5.measurementNoiseCov, cv::Scalar(1e-1));
// Process the point cloud
pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr clustered_cloud (new pcl::PointCloud<pcl::PointXYZ>);
/* Creating the KdTree from input point cloud*/
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
pcl::fromROSMsg (*input, *input_cloud);
tree->setInputCloud (input_cloud);
/* Here we are creating a vector of PointIndices, which contains the actual index
* information in a vector<int>. The indices of each detected cluster are saved here.
* Cluster_indices is a vector containing one instance of PointIndices for each detected
* cluster. Cluster_indices[0] contain all indices of the first cluster in input point cloud.
*/
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (0.08);
ec.setMinClusterSize (10);
ec.setMaxClusterSize (600);
ec.setSearchMethod (tree);
ec.setInputCloud (input_cloud);
/* Extract the clusters out of pc and save indices in cluster_indices.*/
ec.extract (cluster_indices);
/* To separate each cluster out of the vector<PointIndices> we have to
* iterate through cluster_indices, create a new PointCloud for each
* entry and write all points of the current cluster in the PointCloud.
*/
//pcl::PointXYZ origin (0,0,0);
//float mindist_this_cluster = 1000;
//float dist_this_point = 1000;
std::vector<pcl::PointIndices>::const_iterator it;
std::vector<int>::const_iterator pit;
// Vector of cluster pointclouds
std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr > cluster_vec;
for(it = cluster_indices.begin(); it != cluster_indices.end(); ++it) {
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);
for(pit = it->indices.begin(); pit != it->indices.end(); pit++)
{
cloud_cluster->points.push_back(input_cloud->points[*pit]);
//dist_this_point = pcl::geometry::distance(input_cloud->points[*pit],
// origin);
//mindist_this_cluster = std::min(dist_this_point, mindist_this_cluster);
}
cluster_vec.push_back(cloud_cluster);
}
//Ensure at least 6 clusters exist to publish (later clusters may be empty)
while (cluster_vec.size() < 6){
pcl::PointCloud<pcl::PointXYZ>::Ptr empty_cluster (new pcl::PointCloud<pcl::PointXYZ>);
empty_cluster->points.push_back(pcl::PointXYZ(0,0,0));
cluster_vec.push_back(empty_cluster);
}
// Set initial state
KF0.statePre.at<float>(0)=0.0;//initial x pos of the cluster
KF0.statePre.at<float>(1)=0.0;//initial y pos of the cluster
KF0.statePre.at<float>(0)=cluster_vec[0]->points[cluster_vec[0]->points.size()/2].x;
KF0.statePre.at<float>(1)=cluster_vec[0]->points[cluster_vec[0]->points.size()/2].y;
KF0.statePre.at<float>(2)=0;// initial v_x
KF0.statePre.at<float>(3)=0;//initial v_y
// Set initial state
KF1.statePre.at<float>(0)=0.0;//initial x pos of the cluster
KF1.statePre.at<float>(1)=0.0;//initial y pos of the cluster
KF1.statePre.at<float>(0)=cluster_vec[1]->points[cluster_vec[1]->points.size()/2].x;
KF1.statePre.at<float>(1)=cluster_vec[1]->points[cluster_vec[1]->points.size()/2].y;
KF1.statePre.at<float>(2)=0;// initial v_x
KF1.statePre.at<float>(3)=0;//initial v_y
// Set initial state
KF2.statePre.at<float>(0)=0.0;//initial x pos of the cluster
KF2.statePre.at<float>(1)=0.0;//initial y pos of the cluster
KF2.statePre.at<float>(0)=cluster_vec[2]->points[cluster_vec[2]->points.size()/2].x;
KF2.statePre.at<float>(1)=cluster_vec[2]->points[cluster_vec[2]->points.size()/2].y;
KF2.statePre.at<float>(2)=0;// initial v_x
KF2.statePre.at<float>(3)=0;//initial v_y
// Set initial state
KF3.statePre.at<float>(0)=0.0;//initial x pos of the cluster
KF3.statePre.at<float>(1)=0.0;//initial y pos of the cluster
KF3.statePre.at<float>(0)=cluster_vec[3]->points[cluster_vec[3]->points.size()/2].x;
KF3.statePre.at<float>(1)=cluster_vec[3]->points[cluster_vec[3]->points.size()/2].y;
KF3.statePre.at<float>(2)=0;// initial v_x
KF3.statePre.at<float>(3)=0;//initial v_y
// Set initial state
KF4.statePre.at<float>(0)=0.0;//initial x pos of the cluster
KF4.statePre.at<float>(1)=0.0;//initial y pos of the cluster
KF4.statePre.at<float>(0)=cluster_vec[4]->points[cluster_vec[4]->points.size()/2].x;
KF4.statePre.at<float>(1)=cluster_vec[4]->points[cluster_vec[4]->points.size()/2].y;
KF4.statePre.at<float>(2)=0;// initial v_x
KF4.statePre.at<float>(3)=0;//initial v_y
// Set initial state
KF5.statePre.at<float>(0)=0.0;//initial x pos of the cluster
KF5.statePre.at<float>(1)=0.0;//initial y pos of the cluster
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
cout<<"About to setup callback";
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
}
else
{
cout<<"ELSE firstFrame="<<firstFrame<<"\n";
pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr clustered_cloud (new pcl::PointCloud<pcl::PointXYZ>);
/* Creating the KdTree from input point cloud*/
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
pcl::fromROSMsg (*input, *input_cloud);
tree->setInputCloud (input_cloud);
/* Here we are creating a vector of PointIndices, which contains the actual index
* information in a vector<int>. The indices of each detected cluster are saved here.
* Cluster_indices is a vector containing one instance of PointIndices for each detected
* cluster. Cluster_indices[0] contain all indices of the first cluster in input point cloud.
*/
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (0.08);
ec.setMinClusterSize (10);
ec.setMaxClusterSize (600);
ec.setSearchMethod (tree);
ec.setInputCloud (input_cloud);
cout<<"PCL init successfull\n";
/* Extract the clusters out of pc and save indices in cluster_indices.*/
ec.extract (cluster_indices);
cout<<"PCL extract successfull\n";
/* To separate each cluster out of the vector<PointIndices> we have to
* iterate through cluster_indices, create a new PointCloud for each
* entry and write all points of the current cluster in the PointCloud.
*/
//pcl::PointXYZ origin (0,0,0);
//float mindist_this_cluster = 1000;
//float dist_this_point = 1000;
std::vector<pcl::PointIndices>::const_iterator it;
std::vector<int>::const_iterator pit;
// Vector of cluster pointclouds
std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr > cluster_vec;
for(it = cluster_indices.begin(); it != cluster_indices.end(); ++it) {
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);
for(pit = it->indices.begin(); pit != it->indices.end(); pit++)
{
cloud_cluster->points.push_back(input_cloud->points[*pit]);
//dist_this_point = pcl::geometry::distance(input_cloud->points[*pit],
// origin);
//mindist_this_cluster = std::min(dist_this_point, mindist_this_cluster);
}
cluster_vec.push_back(cloud_cluster);
}
cout<<"cluster_vec got some clusters\n";
//Ensure at least 6 clusters exist to publish (later clusters may be empty)
while (cluster_vec.size() < 6){
pcl::PointCloud<pcl::PointXYZ>::Ptr empty_cluster (new pcl::PointCloud<pcl::PointXYZ>);
empty_cluster->points.push_back(pcl::PointXYZ(0,0,0));
cluster_vec.push_back(empty_cluster);
}
std_msgs::Float32MultiArray cc;
for(int i=0;i<6;i++)
{
cc.data.push_back(cluster_vec[i]->points[cluster_vec[i]->points.size()/2].x);
cc.data.push_back(cluster_vec[i]->points[cluster_vec[i]->points.size()/2].y);
cc.data.push_back(cluster_vec[i]->points[cluster_vec[i]->points.size()/2].z);
}
cout<<"6 clusters initialized\n";
//cc_pos.publish(cc);// Publish cluster mid-points.
KFT(cc);
int i=0;
bool publishedCluster[6];
for(auto it=objID.begin();it!=objID.end();it++)
{ cout<<"Inside the for loop\n";
switch(*it)
{
cout<<"Inside the switch case\n";
case 0: {
publish_cloud(pub_cluster0,cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID
i++;
break;
}
case 1: {
publish_cloud(pub_cluster1,cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID
i++;
break;
}
case 2: {
publish_cloud(pub_cluster2,cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID
i++;
break;
}
case 3: {
publish_cloud(pub_cluster3,cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID
i++;
break;
}
case 4: {
publish_cloud(pub_cluster4,cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID
i++;
break;
}
case 5: {
publish_cloud(pub_cluster5,cluster_vec[i]);
publishedCluster[i]=true;//Use this flag to publish only once for a given obj ID
i++;
break;
}
default: break;
}
}
}
}
int main(int argc, char** argv)
{
// ROS init
ros::init (argc,argv,"KFTracker");
ros::NodeHandle nh;
// Publishers to publish the state of the objects (pos and vel)
//objState1=nh.advertise<geometry_msgs::Twist> ("obj_1",1);
cout<<"About to setup callback\n";
// Create a ROS subscriber for the input point cloud
ros::Subscriber sub = nh.subscribe ("filtered_cloud", 1, cloud_cb);
// Create a ROS publisher for the output point cloud
pub_cluster0 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_0", 1);
pub_cluster1 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_1", 1);
pub_cluster2 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_2", 1);
pub_cluster3 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_3", 1);
pub_cluster4 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_4", 1);
pub_cluster5 = nh.advertise<sensor_msgs::PointCloud2> ("cluster_5", 1);
// Subscribe to the clustered pointclouds
ros::Subscriber c1=nh.subscribe("ccs",100,KFT);
//ros::Subscriber c1=nh.subscribe("ccs",100,KFT);
objID_pub = nh.advertise<std_msgs::Int32MultiArray>("obj_id", 1);
/* Point cloud clustering
*/
//cc_pos=nh.advertise<std_msgs::Float32MultiArray>("ccs",100);//clusterCenter1
/* Point cloud clustering
*/
ros::spin();