Skip to content

A Tightly-Coupled System for LiDAR-Inertial Odometry and Multi-Object Tracking.

License

Notifications You must be signed in to change notification settings

tiev-tongji/LIMOT

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

6 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

LIMOT

  • A tightly-coupled multi-object tracking and LiDAR-inertial odometry system, allowing for joint estimation of the poses of both the ego-vehicle and surrounding objects.

drawing

Dependency

We developed LIMOT on Ubuntu 20.04.

  • ROS (Noetic)
    sudo apt-get install -y ros-noetic-navigation
    sudo apt-get install -y ros-noetic-robot-localization
    sudo apt-get install -y ros-noetic-robot-state-publisher
    
  • gtsam (Georgia Tech Smoothing and Mapping library, recommended 4.0.3)
    sudo add-apt-repository ppa:borglab/gtsam-release-4.0
    sudo apt install libgtsam-dev libgtsam-unstable-dev
    

Install

Use the following commands to download and compile the package.

mkdir -p ~/limot_ws/src
cd ~/limot_ws/src
git clone https://github.com/tiev-tongji/LIMOT.git
cd ..
catkin_make -j

Run the package

  1. Run the launch file:
source devel/setup.bash
roslaunch limot run_kitti.launch
  1. Run your object detector, which subscribes to LiDAR scans and publishes the detection results with the formats:

    [timestamp, [type, x, y, z, l, w, h, yaw, score], ...,[type ,x, ..., score]].

  2. Play existing bag files:

rosbag play your-bag.bag

Sample dataset

  • Download the KITTI tracking dataset to test the functionality of the LIMOT. The dataset below are configured to run using the params_kitti.yaml:

  • Download the self-collected dataset to test the functionality of the LIMOT. The dataset below are configured to run using the params_hdl64.yaml:

Paper

  • Our previous work DL-SLOT has been accepted by the IEEE Transactions on Intelligent Vehicles.
@article{tian2023dl,
  title={DL-SLOT: Tightly-Coupled Dynamic LiDAR SLAM and 3D Object Tracking Based on Collaborative Graph Optimization},
  author={Tian, Xuebo and Zhu, Zhongyang and Zhao, Junqiao and Tian, Gengxuan and Ye, Chen},
  journal={IEEE Transactions on Intelligent Vehicles},
  year={2023},
  publisher={IEEE}
}
  • This paper has been submitted to RA-L.

About

A Tightly-Coupled System for LiDAR-Inertial Odometry and Multi-Object Tracking.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages