Towards Robust UAV Tracking in GNSS-Denied Environments: A Multi-LiDAR Multi-UAV Dataset



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We present a novel multi-LiDAR dataset specifically designed for UAV tracking. Our dataset includes data from a spinning LiDAR, two solid-state LiDARs with different Field of View (FoV) and scan patterns, and an RGB-D camera. This diverse sensor suite allows for research on new challenges in the field, including limited FoV adaptability and multi-modality data processing. For a comprehensive list of sequences refer to the paper Towards Robust UAV Tracking in GNSS-Denied Environments: A Multi-LiDAR Multi-UAV Dataset and the project page


Calibration

We provide a ROS package to compute the extrinsic parameters between LiDARs and camera based on GICP. As the OS1 has the largest FOV, it is treated as base reference frame ("base_link") in which all the other point clouds are transformed. For the Avia, Mid-360 and Realsense D435, we integrated the first five frames to increase point cloud density.

To use this package, play the Calibration rosbag from our dataset:

rosbag play Calibration.bag -l

Then run our calibration launch file:

roslaunch multi_lidar_multi_uav_dataset lidars_extrinsic_computation.launch

The computed extrinsic parameters will appear in the terminal:

OS -> base_link 0 0 0 0 0 0 /os_sensor /base_link 10
Avia -> base_link   0.149354  0.0423582 -0.0524961  3.13419 -3.13908 -3.13281 /avia_frame /base_link 10
Mid360 -> base_link   0.125546 -0.0554536   -0.20206 0.00467344  0.0270294  0.0494959 /mid360_frame /base_link 10
Camera -> base_link -0.172863   0.11895 -0.101785 1.55222 3.11188 1.60982 /camera_depth_optical_frame /base_link 10

Install

The code has been tested on Ubuntu 20.04 with ROS Noetic

Dependencies

Build

  cd ~/catkin_ws/src
  git clone https://github.com/TIERS/multi_lidar_multi_uav_dataset 
  cd ..
  catkin build

Citation

If you use this dataset for any academic work, please cite the following publication:

@inproceedings{catalano2023towards,
  title={Towards robust uav tracking in gnss-denied environments: a multi-lidar multi-uav dataset},
  author={Catalano, Iacopo and Yu, Xianjia and Queralta, Jorge Pe{\~n}a},
  booktitle={2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)},
  pages={1--7},
  year={2023},
  organization={IEEE}
}