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this shit ON FIRE

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Liam
2025-08-24 15:24:28 +02:00
parent 77fc1bb186
commit 902de6a12b
6 changed files with 998 additions and 0 deletions
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69
src/target_tracking/CMakeLists.txt Normal file
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cmake_minimum_required(VERSION 3.8)
project(target_tracking)
# Required packages
find_package(ament_cmake REQUIRED)
find_package(rclcpp REQUIRED)
find_package(sensor_msgs REQUIRED)
find_package(pcl_conversions REQUIRED)
find_package(PCL REQUIRED)
find_package(tf2 REQUIRED)
find_package(tf2_ros REQUIRED)
find_package(tf2_sensor_msgs REQUIRED)
find_package(OpenCV REQUIRED)
find_package(visualization_msgs REQUIRED)
# Include paths
include_directories(
include
${PCL_INCLUDE_DIRS}
${OpenCV_INCLUDE_DIRS}
)
# Node for preprocessing
add_executable(cloud_preprocessing_node
src/cloud_preprocessing.cpp
)
# Dependencies for preprocessing
ament_target_dependencies(cloud_preprocessing_node
rclcpp
sensor_msgs
pcl_conversions
tf2
tf2_ros
tf2_sensor_msgs
)
# Node for clustering
add_executable(cloud_clustering_node
src/cloud_clustering.cpp
)
# Dependencies for clustering
ament_target_dependencies(cloud_clustering_node
rclcpp
sensor_msgs
visualization_msgs
PCL
pcl_conversions
OpenCV
)
#target_link_libraries(target_tracking_node ${PCL_LIBRARIES})
# Install target
install(TARGETS
cloud_preprocessing_node
cloud_clustering_node
DESTINATION lib/${PROJECT_NAME}
)
# Install launchfile
install(
DIRECTORY launch/
DESTINATION share/${PROJECT_NAME}/launch
)
ament_package()

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src/target_tracking/README.md Normal file
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# Target tracking
Ros2 implementation of a filtering node in combination with a clustering node to find a target with lidar data

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src/target_tracking/launch/target_tracking_launch.py Normal file
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from launch import LaunchDescription
from launch_ros.actions import Node
import math
################### user configure parameters for ros2 start ###################
# Topics/Frames
frame_id = 'velodyne'
topic_preprocessing_in = 'velodyne_points'
topic_preprocessing_out = 'filtered_points'
# Preprocessing
x_min = 0.0
x_max = 20.0
z_min = -0.5
z_max = 1.5
tan_h_fov = math.pi / 4 # ±45°
tan_v_fov = math.pi / 6 # ±30°
# Clustering
z_dim_scale = 0.2
cluster_tolerance = 0.2
min_cluster_size = 10
max_cluster_size = 1000
################### user configure parameters for ros2 end #####################
cloud_preprocessing_params = [
{"topic_in": topic_preprocessing_in},
{"topic_out": topic_preprocessing_out},
{"x_min": x_min},
{"x_max": x_max},
{"z_min": z_min},
{"z_max": z_max},
{"tan_h_fov": tan_h_fov},
{"tan_v_fov": tan_v_fov}
]
cloud_clustering_params = [
{"topic_in": topic_preprocessing_out},
{"frame_id": frame_id},
{"z_dim_scale": z_dim_scale},
{"cluster_tolerance": cluster_tolerance},
{"min_cluster_size": min_cluster_size},
{"max_cluster_size": max_cluster_size}
]
def generate_launch_description():
return LaunchDescription([
Node(
package='target_tracking',
executable='cloud_preprocessing_node',
name='cloud_preprocessing',
parameters=cloud_preprocessing_params,
output={
'stdout': 'screen',
'stderr': 'screen',
}
),
Node(
package='target_tracking',
executable='cloud_clustering_node',
name='cloud_clustering',
parameters=cloud_clustering_params,
output={
'stdout': 'screen',
'stderr': 'screen',
}
)
])

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src/target_tracking/package.xml Normal file
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<?xml version="1.0"?>
<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
<package format="3">
<name>target_tracking</name>
<version>1.0.1</version>
<description>Studienprojekt</description>
<maintainer email="liam20030625@gmail.com">Liam</maintainer>
<license>Apache 2.0</license>
<buildtool_depend>ament_cmake</buildtool_depend>
<test_depend>ament_lint_auto</test_depend>
<test_depend>ament_lint_common</test_depend>
<!-- Dependencies for voxel -->
<depend>rclcpp</depend>
<depend>sensor_msgs</depend>
<depend>visualization_msgs</depend>
<depend>pcl_conversions</depend>
<depend>PCL</depend>
<depend>cv_bridge</depend>
<depend>OpenCV</depend>
<export>
<build_type>ament_cmake</build_type>
</export>
</package>

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src/target_tracking/src/cloud_clustering.cpp Normal file
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#include <pcl_conversions/pcl_conversions.h>
#include <sensor_msgs/msg/point_cloud2.hpp>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <algorithm>
#include <fstream>
#include <geometry_msgs/msg/point.hpp>
#include <iostream>
#include <iterator>
#include <opencv2/video/video.hpp>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <rclcpp/rclcpp.hpp>
#include <std_msgs/msg/float32_multi_array.hpp>
#include <std_msgs/msg/int32_multi_array.hpp>
#include <string.h>
#include <pcl/common/centroid.h>
#include <pcl/common/geometry.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl_conversions/pcl_conversions.h>
#include <sensor_msgs/msg/point_cloud2.hpp>
#include <limits>
#include <utility>
#include <visualization_msgs/msg/marker.hpp>
#include <visualization_msgs/msg/marker_array.hpp>
using namespace std;
using namespace cv;
rclcpp::Publisher<std_msgs::msg::Int32MultiArray>::SharedPtr objID_pub;
// KF init
int stateDim = 4; // [x,y,v_x,v_y]//,w,h]
int measDim = 2; // [z_x,z_y,z_w,z_h]
int ctrlDim = 0;
std::string frame_id;
std::string topic_in;
float z_dim_scale;
float cluster_tolerance;
int min_cluster_size;
int max_cluster_size;
cv::KalmanFilter KF0(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF1(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF2(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF3(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF4(stateDim, measDim, ctrlDim, CV_32F);
cv::KalmanFilter KF5(stateDim, measDim, ctrlDim, CV_32F);
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pub_cluster0;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pub_cluster1;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pub_cluster2;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pub_cluster3;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pub_cluster4;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pub_cluster5;
rclcpp::Publisher<visualization_msgs::msg::MarkerArray>::SharedPtr markerPub;
std::vector<geometry_msgs::msg::Point> prevClusterCenters;
cv::Mat state(stateDim, 1, CV_32F);
cv::Mat_<float> measurement(2, 1);
std::vector<int> objID; // Output of the data association using KF
// measurement.setTo(Scalar(0));
bool firstFrame = true;
// calculate euclidean distance of two points
double euclidean_distance(geometry_msgs::msg::Point &p1, geometry_msgs::msg::Point &p2) {
return sqrt((p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y - p2.y) +
(p1.z - p2.z) * (p1.z - p2.z) * z_dim_scale);
}
/*
//Count unique object IDs. just to make sure same ID has not been assigned to
two KF_Trackers. int countIDs(vector<int> v)
{
transform(v.begin(), v.end(), v.begin(), abs); // O(n) where n =
distance(v.end(), v.begin()) sort(v.begin(), v.end()); // Average case O(n log
n), worst case O(n^2) (usually implemented as quicksort.
// To guarantee worst case O(n log n) replace with make_heap, then
sort_heap.
// Unique will take a sorted range, and move things around to get duplicated
// items to the back and returns an iterator to the end of the unique
section of the range auto unique_end = unique(v.begin(), v.end()); // Again n
comparisons return distance(unique_end, v.begin()); // Constant time for random
access iterators (like vector's)
}
*/
/*
objID: vector containing the IDs of the clusters that should be associated with
each KF_Tracker objID[0] corresponds to KFT0, objID[1] corresponds to KFT1 etc.
*/
std::pair<int, int> findIndexOfMin(std::vector<std::vector<float>> distMat) {
std::pair<int, int> minIndex;
float minEl = std::numeric_limits<float>::max();
for (int i = 0; i < distMat.size(); i++)
for (int j = 0; j < distMat.at(0).size(); j++) {
if (distMat[i][j] < minEl) {
minEl = distMat[i][j];
minIndex = std::make_pair(i, j);
}
}
return minIndex;
}
void KFT(const std_msgs::msg::Float32MultiArray ccs) {
// First predict, to update the internal statePre variable
std::vector<cv::Mat> pred{KF0.predict(), KF1.predict(), KF2.predict(),
KF3.predict(), KF4.predict(), KF5.predict()};
// cv::Point predictPt(prediction.at<float>(0),prediction.at<float>(1));
// cout<<"Prediction 1
// ="<<prediction.at<float>(0)<<","<<prediction.at<float>(1)<<"\n";
// Get measurements
// Extract the position of the clusters forom the multiArray. To check if the
// data coming in, check the .z (every third) coordinate and that will be 0.0
std::vector<geometry_msgs::msg::Point> clusterCenters; // clusterCenters
int i = 0;
for (std::vector<float>::const_iterator it = ccs.data.begin();
it != ccs.data.end(); it += 3) {
geometry_msgs::msg::Point pt;
pt.x = *it;
pt.y = *(it + 1);
pt.z = *(it + 2);
clusterCenters.push_back(pt);
}
std::vector<geometry_msgs::msg::Point> KFpredictions;
i = 0;
for (auto it = pred.begin(); it != pred.end(); it++) {
geometry_msgs::msg::Point pt;
pt.x = (*it).at<float>(0);
pt.y = (*it).at<float>(1);
pt.z = (*it).at<float>(2);
KFpredictions.push_back(pt);
}
// Find the cluster that is more probable to be belonging to a given KF.
objID.clear(); // Clear the objID vector
objID.resize(6); // Allocate default elements so that [i] doesnt segfault.
// Should be done better
// Copy clusterCentres for modifying it and preventing multiple assignments of
// the same ID
std::vector<geometry_msgs::msg::Point> copyOfClusterCenters(clusterCenters);
std::vector<std::vector<float>> distMat;
for (int filterN = 0; filterN < 6; filterN++) {
std::vector<float> distVec;
for (int n = 0; n < 6; n++) {
distVec.push_back(
euclidean_distance(KFpredictions[filterN], copyOfClusterCenters[n]));
}
distMat.push_back(distVec);
/*// Based on distVec instead of distMat (global min). Has problems with the
person's leg going out of scope int
ID=std::distance(distVec.begin(),min_element(distVec.begin(),distVec.end()));
//cout<<"finterlN="<<filterN<<" minID="<<ID
objID.push_back(ID);
// Prevent assignment of the same object ID to multiple clusters
copyOfClusterCenters[ID].x=100000;// A large value so that this center is
not assigned to another cluster copyOfClusterCenters[ID].y=10000;
copyOfClusterCenters[ID].z=10000;
*/
//cout << "filterN=" << filterN << "\n";
}
for (int clusterCount = 0; clusterCount < 6; clusterCount++) {
// 1. Find min(distMax)==> (i,j);
std::pair<int, int> minIndex(findIndexOfMin(distMat));
// 2. objID[i]=clusterCenters[j]; counter++
objID[minIndex.first] = minIndex.second;
// 3. distMat[i,:]=10000; distMat[:,j]=10000
distMat[minIndex.first] =
std::vector<float>(6, 10000.0); // Set the row to a high number.
for (int row = 0; row < distMat.size();
row++) // set the column to a high number
{
distMat[row][minIndex.second] = 10000.0;
}
// 4. if(counter<6) got to 1.
//cout << "clusterCount=" << clusterCount << "\n";
}
// cout<<"Got object IDs"<<"\n";
// countIDs(objID);// for verif/corner cases
// display objIDs
/* DEBUG
cout<<"objID= ";
for(auto it=objID.begin();it!=objID.end();it++)
cout<<*it<<" ,";
cout<<"\n";
*/
visualization_msgs::msg::MarkerArray clusterMarkers;
for (int i = 0; i < 6; i++) {
visualization_msgs::msg::Marker m;
m.id = i;
m.type = visualization_msgs::msg::Marker::CUBE;
m.header.frame_id = frame_id;
m.scale.x = 0.3;
m.scale.y = 0.3;
m.scale.z = 0.3;
m.action = visualization_msgs::msg::Marker::ADD;
m.color.a = 1.0;
m.color.r = i % 2 ? 1 : 0;
m.color.g = i % 3 ? 1 : 0;
m.color.b = i % 4 ? 1 : 0;
// geometry_msgs::msg::Point clusterC(clusterCenters.at(objID[i]));
geometry_msgs::msg::Point clusterC(KFpredictions[i]);
m.pose.position.x = clusterC.x;
m.pose.position.y = clusterC.y;
m.pose.position.z = clusterC.z;
clusterMarkers.markers.push_back(m);
}
prevClusterCenters = clusterCenters;
markerPub->publish(clusterMarkers);
std_msgs::msg::Int32MultiArray obj_id;
for (auto it = objID.begin(); it != objID.end(); it++)
obj_id.data.push_back(*it);
// Publish the object IDs
objID_pub->publish(obj_id);
// convert clusterCenters from geometry_msgs::msg::Point to floats
std::vector<std::vector<float>> cc;
for (int i = 0; i < 6; i++) {
vector<float> pt;
pt.push_back(clusterCenters[objID[i]].x);
pt.push_back(clusterCenters[objID[i]].y);
pt.push_back(clusterCenters[objID[i]].z);
cc.push_back(pt);
}
// cout<<"cc[5][0]="<<cc[5].at(0)<<"cc[5][1]="<<cc[5].at(1)<<"cc[5][2]="<<cc[5].at(2)<<"\n";
float meas0[2] = {cc[0].at(0), cc[0].at(1)};
float meas1[2] = {cc[1].at(0), cc[1].at(1)};
float meas2[2] = {cc[2].at(0), cc[2].at(1)};
float meas3[2] = {cc[3].at(0), cc[3].at(1)};
float meas4[2] = {cc[4].at(0), cc[4].at(1)};
float meas5[2] = {cc[5].at(0), cc[5].at(1)};
// The update phase
cv::Mat meas0Mat = cv::Mat(2, 1, CV_32F, meas0);
cv::Mat meas1Mat = cv::Mat(2, 1, CV_32F, meas1);
cv::Mat meas2Mat = cv::Mat(2, 1, CV_32F, meas2);
cv::Mat meas3Mat = cv::Mat(2, 1, CV_32F, meas3);
cv::Mat meas4Mat = cv::Mat(2, 1, CV_32F, meas4);
cv::Mat meas5Mat = cv::Mat(2, 1, CV_32F, meas5);
// cout<<"meas0Mat"<<meas0Mat<<"\n";
if (!(meas0Mat.at<float>(0, 0) == 0.0f || meas0Mat.at<float>(1, 0) == 0.0f))
Mat estimated0 = KF0.correct(meas0Mat);
if (!(meas1[0] == 0.0f || meas1[1] == 0.0f))
Mat estimated1 = KF1.correct(meas1Mat);
if (!(meas2[0] == 0.0f || meas2[1] == 0.0f))
Mat estimated2 = KF2.correct(meas2Mat);
if (!(meas3[0] == 0.0f || meas3[1] == 0.0f))
Mat estimated3 = KF3.correct(meas3Mat);
if (!(meas4[0] == 0.0f || meas4[1] == 0.0f))
Mat estimated4 = KF4.correct(meas4Mat);
if (!(meas5[0] == 0.0f || meas5[1] == 0.0f))
Mat estimated5 = KF5.correct(meas5Mat);
// Publish the point clouds belonging to each clusters
// cout<<"estimate="<<estimated.at<float>(0)<<","<<estimated.at<float>(1)<<"\n";
// Point statePt(estimated.at<float>(0),estimated.at<float>(1));
// cout<<"DONE KF_TRACKER\n";
}
void publish_cloud(
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pub,
pcl::PointCloud<pcl::PointXYZI>::Ptr cluster,
rclcpp::Node::SharedPtr node)
{
// Create a PointCloud2 message
auto clustermsg = std::make_shared<sensor_msgs::msg::PointCloud2>();
// Convert PCL cloud to ROS 2 message
pcl::toROSMsg(*cluster, *clustermsg);
// Set header info
clustermsg->header.frame_id = frame_id;
clustermsg->header.stamp = node->now();
// Publish the message
pub->publish(*clustermsg);
}
void parse_cloud(const sensor_msgs::msg::PointCloud2::SharedPtr input,
std::vector<std::pair<
pcl::PointCloud<
pcl::PointXYZI
>::Ptr
,float>>& cluster_vec,
std::vector<pcl::PointXYZI>& clusterCentroids
) {
pcl::PointCloud<pcl::PointXYZI>::Ptr input_a(new pcl::PointCloud<pcl::PointXYZI>);
pcl::fromROSMsg(*input, *input_a);
// Apply filters
pcl::PointCloud<pcl::PointXYZI>::Ptr fov_filtered(new pcl::PointCloud<pcl::PointXYZI>);
fov_filtered->reserve(input_a->size());
for (const auto& point : input_a->points) {
if (point.x < 0 || point.x > 40) continue;
if (isnan(point.x) || isnan(point.y) || isnan(point.z)) continue;
if (point.z < -0.5 || point.z > 1.5) continue;
fov_filtered->points.push_back(point); // emplace_back also works
}
if (fov_filtered->empty()) return; // nothing to cluster
pcl::PointCloud<pcl::PointXYZI>::Ptr input_cloud = fov_filtered;
// Setup KdTree
pcl::search::KdTree<pcl::PointXYZI>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZI>);
tree->setInputCloud(input_cloud);
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZI> ec;
ec.setClusterTolerance(cluster_tolerance);
ec.setMinClusterSize(min_cluster_size);
ec.setMaxClusterSize(max_cluster_size);
ec.setSearchMethod(tree);
ec.setInputCloud(input_cloud);
/* Extract the clusters out of pc and save indices in cluster_indices.*/
ec.extract(cluster_indices);
std::vector<pcl::PointIndices>::const_iterator it;
std::vector<int>::const_iterator pit;
for (it = cluster_indices.begin(); it != cluster_indices.end(); ++it) {
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_cluster(
new pcl::PointCloud<pcl::PointXYZI>);
float x = 0.0;
float y = 0.0;
int numPts = 0;
float intensity_sum = 0.0;
for (pit = it->indices.begin(); pit != it->indices.end(); pit++) {
cloud_cluster->points.push_back(input_cloud->points[*pit]);
x += input_cloud->points[*pit].x;
y += input_cloud->points[*pit].y;
numPts++;
intensity_sum += input_cloud->points[*pit].intensity;
// dist_this_point = pcl::geometry::distance(input_cloud->points[*pit],
// origin);
// mindist_this_cluster = std::min(dist_this_point,
// mindist_this_cluster);
}
if (numPts == 0) continue;
pcl::PointXYZI centroid;
centroid.x = x / numPts;
centroid.y = y / numPts;
centroid.z = 0.0;
cluster_vec.push_back(std::make_pair(cloud_cluster, intensity_sum / numPts));
// Get the centroid of the cluster
clusterCentroids.push_back(centroid);
}
// Ensure at least 6 clusters exist to publish (later clusters may be empty)
while (cluster_vec.size() < 6) {
pcl::PointCloud<pcl::PointXYZI>::Ptr empty_cluster(
new pcl::PointCloud<pcl::PointXYZI>);
pcl::PointXYZI pt;
pt.x = pt.y = pt.z = pt.intensity = 0.0f;
empty_cluster->points.push_back(pt);
pcl::PointXYZI centroid;
centroid.x = 0.0;
centroid.y = 0.0;
centroid.z = 0.0;
centroid.intensity = 0.0;
cluster_vec.push_back(std::make_pair(empty_cluster, 0.0f));
}
while (clusterCentroids.size() < 6) {
pcl::PointXYZI centroid;
centroid.x = 0.0;
centroid.y = 0.0;
centroid.z = 0.0;
centroid.intensity = 0.0;
clusterCentroids.push_back(centroid);
}
}
void cloud_cb(const sensor_msgs::msg::PointCloud2::SharedPtr input, rclcpp::Node::SharedPtr node)
{
// Vector of cluster pointclouds
std::vector<std::pair<pcl::PointCloud<pcl::PointXYZI>::Ptr,float>> cluster_vec;
// Cluster centroids
std::vector<pcl::PointXYZI> clusterCentroids;
if (firstFrame) {
// If this is the first frame, initialize kalman filters for the clustered objects
// 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
float dvx = 0.01f; // 1.0
float dvy = 0.01f; // 1.0
float dx = 1.0f;
float dy = 1.0f;
KF0.transitionMatrix = (Mat_<float>(4, 4) << dx, 0, 1, 0, 0, dy, 0, 1, 0, 0,
dvx, 0, 0, 0, 0, dvy);
KF1.transitionMatrix = (Mat_<float>(4, 4) << dx, 0, 1, 0, 0, dy, 0, 1, 0, 0,
dvx, 0, 0, 0, 0, dvy);
KF2.transitionMatrix = (Mat_<float>(4, 4) << dx, 0, 1, 0, 0, dy, 0, 1, 0, 0,
dvx, 0, 0, 0, 0, dvy);
KF3.transitionMatrix = (Mat_<float>(4, 4) << dx, 0, 1, 0, 0, dy, 0, 1, 0, 0,
dvx, 0, 0, 0, 0, dvy);
KF4.transitionMatrix = (Mat_<float>(4, 4) << dx, 0, 1, 0, 0, dy, 0, 1, 0, 0,
dvx, 0, 0, 0, 0, dvy);
KF5.transitionMatrix = (Mat_<float>(4, 4) << dx, 0, 1, 0, 0, dy, 0, 1, 0, 0,
dvx, 0, 0, 0, 0, dvy);
cv::setIdentity(KF0.measurementMatrix);
cv::setIdentity(KF1.measurementMatrix);
cv::setIdentity(KF2.measurementMatrix);
cv::setIdentity(KF3.measurementMatrix);
cv::setIdentity(KF4.measurementMatrix);
cv::setIdentity(KF5.measurementMatrix);
// Process Noise Covariance Matrix Q
// [ Ex 0 0 0 0 0 ]
// [ 0 Ey 0 0 0 0 ]
// [ 0 0 Ev_x 0 0 0 ]
// [ 0 0 0 1 Ev_y 0 ]
//// [ 0 0 0 0 1 Ew ]
//// [ 0 0 0 0 0 Eh ]
float sigmaP = 0.01;
float sigmaQ = 0.1;
setIdentity(KF0.processNoiseCov, Scalar::all(sigmaP));
setIdentity(KF1.processNoiseCov, Scalar::all(sigmaP));
setIdentity(KF2.processNoiseCov, Scalar::all(sigmaP));
setIdentity(KF3.processNoiseCov, Scalar::all(sigmaP));
setIdentity(KF4.processNoiseCov, Scalar::all(sigmaP));
setIdentity(KF5.processNoiseCov, Scalar::all(sigmaP));
// Meas noise cov matrix R
cv::setIdentity(KF0.measurementNoiseCov, cv::Scalar(sigmaQ)); // 1e-1
cv::setIdentity(KF1.measurementNoiseCov, cv::Scalar(sigmaQ));
cv::setIdentity(KF2.measurementNoiseCov, cv::Scalar(sigmaQ));
cv::setIdentity(KF3.measurementNoiseCov, cv::Scalar(sigmaQ));
cv::setIdentity(KF4.measurementNoiseCov, cv::Scalar(sigmaQ));
cv::setIdentity(KF5.measurementNoiseCov, cv::Scalar(sigmaQ));
// Process the point cloud
parse_cloud(input, cluster_vec, clusterCentroids);
// Set initial state
KF0.statePre.at<float>(0) = clusterCentroids.at(0).x;
KF0.statePre.at<float>(1) = clusterCentroids.at(0).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) = clusterCentroids.at(1).x;
KF1.statePre.at<float>(1) = clusterCentroids.at(1).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) = clusterCentroids.at(2).x;
KF2.statePre.at<float>(1) = clusterCentroids.at(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) = clusterCentroids.at(3).x;
KF3.statePre.at<float>(1) = clusterCentroids.at(3).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) = clusterCentroids.at(4).x;
KF4.statePre.at<float>(1) = clusterCentroids.at(4).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) = clusterCentroids.at(5).x;
KF5.statePre.at<float>(1) = clusterCentroids.at(5).y;
KF5.statePre.at<float>(2) = 0; // initial v_x
KF5.statePre.at<float>(3) = 0; // initial v_y
firstFrame = false;
for (int i = 0; i < 6; i++) {
geometry_msgs::msg::Point pt;
pt.x = clusterCentroids.at(i).x;
pt.y = clusterCentroids.at(i).y;
prevClusterCenters.push_back(pt);
}
}
else {
parse_cloud(input, cluster_vec, clusterCentroids);
std_msgs::msg::Float32MultiArray cc;
for (int i = 0; i < 6; i++) {
cc.data.push_back(clusterCentroids.at(i).x);
cc.data.push_back(clusterCentroids.at(i).y);
cc.data.push_back(clusterCentroids.at(i).z);
}
// cc_pos->publish(cc);// Publish cluster mid-points.
KFT(cc);
int i = 0;
std::sort(objID.begin(), objID.end(), [cluster_vec](const int &a, const int &b) {
return cluster_vec[a].second < cluster_vec[b].second;
});
for (auto it = objID.begin(); it != objID.end(); it++) {
switch (i) {
case 0: {
publish_cloud(pub_cluster0, cluster_vec[*it].first, node);
i++;
break;
}
case 1: {
publish_cloud(pub_cluster1, cluster_vec[*it].first, node);
i++;
break;
}
case 2: {
publish_cloud(pub_cluster2, cluster_vec[*it].first, node);
i++;
break;
}
case 3: {
publish_cloud(pub_cluster3, cluster_vec[*it].first, node);
i++;
break;
}
case 4: {
publish_cloud(pub_cluster4, cluster_vec[*it].first, node);
i++;
break;
}
case 5: {
publish_cloud(pub_cluster5, cluster_vec[*it].first, node);
i++;
break;
}
default:
break;
}
}
}
}
int main(int argc, char **argv) {
// ROS init
rclcpp::init(argc, argv);
auto node = rclcpp::Node::make_shared("cloud_clustering");
//Topic parameters
node->declare_parameter<std::string>("topic_in", "!defaultTopic!");
node->declare_parameter<std::string>("frame_id", "!defaultFrameId!");
//Cluster parameters
node->declare_parameter<float>("z_dim_scale", 0);
node->declare_parameter<float>("cluster_tolerance", 0.0f);
node->declare_parameter<int>("min_cluster_size", 0);
node->declare_parameter<int>("max_cluster_size", 0);
node->get_parameter("topic_in", topic_in);
node->get_parameter("frame_id", frame_id);
node->get_parameter("z_dim_scale", z_dim_scale);
node->get_parameter("cluster_tolerance", cluster_tolerance);
node->get_parameter("min_cluster_size", min_cluster_size);
node->get_parameter("max_cluster_size", max_cluster_size);
// Initialize publishers
objID_pub = node->create_publisher<std_msgs::msg::Int32MultiArray>("object_ids", 10);
pub_cluster0 = node->create_publisher<sensor_msgs::msg::PointCloud2>("cluster0", 10);
pub_cluster1 = node->create_publisher<sensor_msgs::msg::PointCloud2>("cluster1", 10);
pub_cluster2 = node->create_publisher<sensor_msgs::msg::PointCloud2>("cluster2", 10);
pub_cluster3 = node->create_publisher<sensor_msgs::msg::PointCloud2>("cluster3", 10);
pub_cluster4 = node->create_publisher<sensor_msgs::msg::PointCloud2>("cluster4", 10);
pub_cluster5 = node->create_publisher<sensor_msgs::msg::PointCloud2>("cluster5", 10);
markerPub = node->create_publisher<visualization_msgs::msg::MarkerArray>("markers", 10);
// 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
auto sub = node->create_subscription<sensor_msgs::msg::PointCloud2>(
topic_in,
10, // queue size
[node](sensor_msgs::msg::PointCloud2::SharedPtr msg) {
cloud_cb(msg, node);
});
// Publishers
objID_pub = node->create_publisher<std_msgs::msg::Int32MultiArray>("obj_id", 10);
markerPub = node->create_publisher<visualization_msgs::msg::MarkerArray>("viz", 10);
cout << "Started clustering node with parameters:\n"
<< "topic_in: " << topic_in << "\n"
<< "frame_id: " << frame_id << "\n"
<< "z_dim_scale: " << z_dim_scale << "\n"
<< "cluster_tolerance: " << cluster_tolerance << "\n"
<< "min_cluster_size: " << min_cluster_size << "\n"
<< "max_cluster_size: " << max_cluster_size << "\n";
// Example if you still need cluster centers publisher:
// cc_pos = node->create_publisher<std_msgs::msg::Float32MultiArray>("ccs", 10);
/* Point cloud clustering
*/
rclcpp::spin(node);
rclcpp::shutdown();
return 0;
}

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src/target_tracking/src/cloud_preprocessing.cpp Normal file
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#include <rclcpp/rclcpp.hpp>
#include <sensor_msgs/msg/point_cloud2.hpp>
#include <tf2_ros/transform_listener.h>
#include <tf2_ros/buffer.h>
#include <tf2_sensor_msgs/tf2_sensor_msgs.hpp>
#include <cmath>
#include <random>
#include <pcl_conversions/pcl_conversions.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/ModelCoefficients.h>
#include <pcl/io/pcd_io.h>
class CloudFilterNode : public rclcpp::Node
{
//Topic parameters
std::string topic_in;
std::string topic_out;
// Filter parameters
float x_min;
float x_max;
float z_min;
float z_max;
float tan_h_fov;
float tan_v_fov;
public:
CloudFilterNode()
: Node("cloud_preprocessing"), tf_buffer_(this->get_clock()), tf_listener_(tf_buffer_)
{
//Topic parameters
this->declare_parameter<std::string>("topic_in", "test");
this->declare_parameter<std::string>("topic_out", "test2");
//Filter parameters
this->declare_parameter<float>("x_min", 0.0f);
this->declare_parameter<float>("x_max", 0.0f);
this->declare_parameter<float>("z_min", 0.0f);
this->declare_parameter<float>("z_max", 0.0f);
this->declare_parameter<float>("tan_h_fov", 0.0f); // ±45°
this->declare_parameter<float>("tan_v_fov", 0.0f); // ±30°
this->get_parameter("topic_in", topic_in);
this->get_parameter("topic_out", topic_out);
this->get_parameter("x_min", x_min);
this->get_parameter("x_max", x_max);
this->get_parameter("z_min", z_min);
this->get_parameter("z_max", z_max);
this->get_parameter("tan_h_fov", tan_h_fov);
this->get_parameter("tan_v_fov", tan_v_fov);
RCLCPP_INFO(this->get_logger(), "Starting filter node with parameters:\n"
"topic_in: %s\n"
"topic_out: %s\n"
"x_min: %f\n"
"x_max: %f\n"
"z_min: %f\n"
"z_max: %f\n"
"tan_h_fov: %f\n"
"tan_v_fov: %f"
, topic_in.c_str(), topic_out.c_str(), x_min, x_max, z_min, z_max, tan_h_fov, tan_v_fov
);
subscription_ = this->create_subscription<sensor_msgs::msg::PointCloud2>(
topic_in, rclcpp::SensorDataQoS(),
std::bind(&CloudFilterNode::cloud_callback, this, std::placeholders::_1));
filtered_publisher_ = this->create_publisher<sensor_msgs::msg::PointCloud2>(topic_out, 10);
}
private:
void cloud_callback(const sensor_msgs::msg::PointCloud2::SharedPtr msg)
{
// Convert to PCL
pcl::PointCloud<pcl::PointXYZI>::Ptr input(new pcl::PointCloud<pcl::PointXYZI>);
pcl::fromROSMsg(*msg, *input);
pcl::PointCloud<pcl::PointXYZI>::Ptr fov_filtered(new pcl::PointCloud<pcl::PointXYZI>);
fov_filtered->reserve(input->size());
for (const auto& point : input->points)
{
if (point.x < x_min || point.x > x_max) continue;
if (isnan(point.x) || isnan(point.y) || isnan(point.z)) continue;
float inv_x = 1.0f / point.x;
if (std::abs(point.y * inv_x) > tan_h_fov) continue;
if (std::abs(point.z * inv_x) > tan_v_fov) continue;
if (point.z < z_min || point.z > z_max) continue;
fov_filtered->emplace_back(point);
}
// Apply voxel grid filter
pcl::VoxelGrid<pcl::PointXYZI> voxel_filter;
voxel_filter.setInputCloud(fov_filtered);
voxel_filter.setLeafSize(0.03f, 0.03f, 0.03f); // Adjust as needed
pcl::PointCloud<pcl::PointXYZI>::Ptr voxel_filtered(new pcl::PointCloud<pcl::PointXYZI>);
voxel_filter.filter(*voxel_filtered);
//Apply ransac
pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
// Create the segmentation object
pcl::SACSegmentation<pcl::PointXYZI> seg;
// Optional
seg.setOptimizeCoefficients(true);
// Mandatory
seg.setModelType(pcl::SACMODEL_PLANE);
seg.setMethodType(pcl::SAC_RANSAC);
seg.setDistanceThreshold(0.01);
seg.setInputCloud(voxel_filtered);
seg.segment (*inliers, *coefficients);
if (inliers->indices.size () == 0)
{
RCLCPP_INFO(this->get_logger(), "Oh oh. No inliers");
return;
}
//Select remaining points
pcl::ExtractIndices<pcl::PointXYZI> extract;
extract.setInputCloud(voxel_filtered);
extract.setIndices(inliers);
extract.setNegative(true);
extract.filter(*voxel_filtered);
// Convert and publish
sensor_msgs::msg::PointCloud2 out_msg;
pcl::toROSMsg(*voxel_filtered, out_msg);
out_msg.header.stamp = this->get_clock()->now();
out_msg.header.frame_id = msg->header.frame_id;
filtered_publisher_->publish(out_msg);
RCLCPP_INFO(this->get_logger(), "Filtered %zu -> %zu points", input->points.size(), voxel_filtered->points.size());
}
tf2_ros::Buffer tf_buffer_;
tf2_ros::TransformListener tf_listener_;
rclcpp::Subscription<sensor_msgs::msg::PointCloud2>::SharedPtr subscription_;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr filtered_publisher_;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr original_publisher_;
};
int main(int argc, char * argv[])
{
srand((unsigned int) time (NULL));
rclcpp::init(argc, argv);
rclcpp::spin(std::make_shared<CloudFilterNode>());
rclcpp::shutdown();
return 0;
}