This project track the person in indoor environment using 3D LiDAR.
Light Detection and Ranging (LiDAR) technology is now becoming the main tool in many applications such as autonomous driving and human robot collaboration. Point-cloud based 3D object detection is getting popular and widely accepted in industry and everyday life due to its effectiveness against cameras in challenging environment. In this paper, we present a modular approach to detect, track and classify persons using a 3D LiDAR sensor. It combines multiple principles: a robust implementation for object segmentation, a classifier with local geometric descriptors and a tracking solution. Moreover, we achieve a real time solution in a low-performance machine by reducing the amount of points to be processed by obtaining and predicting regions of interest via movement detection and motion prediction without any previous knowledge of the environment. Furthermore, our prototype is able to successfully detect and track persons consistently even in challenging cases due to limitations on the sensor field of view or extreme pose changes such as crouching, jumping, and stretching. Lastly, the proposed solution is tested and evaluated in multiple real 3D LiDAR sensor recordings taken in indoor environment. The results show great potential, particularly a high confidence in positive classifications of human body as compared to state-of-the art approaches.
ROS Package:
cd ~/catkin_ws/src
clone the package
git clone https://github.com/baberjunaid/LiDAR_Tracking_3D.git
finally build the package
cd ~/catkin_ws/
catkin_make
source devel/setup.bash
To run the launch file
roslaunch LiDAR_Tracking_3D Track_3D.launch input:=/ouster/points
where /ouster/points contains the node of point cloud. We have used Ouster lidar. If you are using /velodyne_points then update the value of input accordingly.
BAG Files:
. Sample 1
. Other sample files to be uploaded soon
This project tracks multiple people in an indoor environment using 3D LiDAR (Ouster) in real-time. For a given 3D point cloud, ROI is extracted that reduces the number of point clouds, ROI Demo. Later, these ROIS are passed to voxelization and segmentation. The segmented objects later classified into person and on-person. The demo of person tracking and detection can be seen on Tracking Demo.