With the increasing prevalence of drones in various industries, the navigation and tracking of unmanned aerial vehicles (UAVs) in challenging environments, particularly GNSS-denied areas, have become crucial concerns. To address this need, 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.
The dataset facilitates the evaluation of existing algorithms and the development of new ones, paving the way for advances in UAV tracking techniques. Notably, we provide data in both indoor and outdoor environments. We also consider variable UAV sizes, from micro-aerial vehicles to more standard commercial UAV platforms. The outdoor trajectories are selected with close proximity to buildings, targeting research in UAV detection in urban areas, e.g., within counter-UAV systems or docking for UAV logistics.
In addition to the dataset, we provide a baseline comparison with recent LiDAR-based UAV tracking algorithms, benchmarking the performance with different sensors, UAVs, and algorithms. Importantly, our dataset shows that current methods have shortcomings and are unable to track UAVs consistently across different scenarios.
The main contributions of this work and the presented dataset are the following:
Our dataset is organized into three distinct categories based on the environment and trajectory structure: structured indoor, unstructured indoor, and unstructured outdoor. Each category captures specific movement patterns and characteristics, as follows:
The data collection makes use of the rosbag format. For each type of data included in the dataset, the data format is as follows:
Sequence | Description | Total Size | Duration | Difficulty | Rosbag |
---|---|---|---|---|---|
HolybroStnd01 | Structured (Up/Down) | 8.5 GB | 31.6s | Easy | Rosbag |
HolybroStnd02 | Structured (Square) | 24.4 GB | 90s | Easy | Rosbag |
HolybroStnd03 | Structured (Circle) | 20.6 GB | 76s | Easy | Rosbag |
HolybroStnd04 | Structured (Spiral) | 26.5 GB | 98s | Easy | Rosbag |
Holybro01 | Unstructured, Indoor | 18.5 GB | 68s | Easy | Rosbag |
Holybro02 | Unstructured, Indoor | 19.4 GB | 72s | Easy | Rosbag |
Holybro03 | Unstructured, Indoor | 21.8 GB | 81s | Easy | Rosbag |
Holybro04 | Unstructured, Indoor | 20.8 GB | 77s | Medium | Rosbag |
Holybro05 | Unstructured, Indoor | 25.0 GB | 93s | Medium | Rosbag |
HolybroOut01 | Unstructured, Outdoor | 10.1 GB | 37.8s | Medium | Rosbag |
HolybroOut02 | Unstructured, Outdoor | 10.9 GB | 40.6s | Medium | Rosbag |
Autel01 | Unstructured, Indoor | 11.1 GB | 41.4s | Easy | Rosbag |
Autel02 | Unstructured, Indoor | 16.3 GB | 60s | Easy | Rosbag |
Autel03 | Unstructured, Indoor | 13.0 GB | 48.6s | Easy | Rosbag |
Autel04 | Unstructured, Indoor | 12.7 GB | 47.3s | Medium | Rosbag |
Autel05 | Unstructured, Indoor | 13.5 GB | 50.2s | Hard | Rosbag |
AutelOut01 | Unstructured, Outdoor | 10.1 GB | 37.6s | Hard | Rosbag |
AutelOut02 | Unstructured, Outdoor | 11.1 GB | 41.3s | Hard | Rosbag |
Tello01 | Unstructured, Indoor | 13.4 GB | 49.9s | Medium | Rosbag |
Tello02 | Unstructured, Indoor | 15.5 GB | 57.8s | Medium | Rosbag |
Tello03 | Unstructured, Indoor | 15.5 GB | 57.8s | Hard | Rosbag |
Tello04 | Unstructured, Indoor | 18.7 GB | 69s | Hard | Rosbag |
Tello05 | Unstructured, Indoor | 14.5 GB | 54.1s | Hard | Rosbag |
TelloOut01 | Unstructured, Outdoor | 10.3 GB | 38.4s | Hard | Rosbag |
TelloOut02 | Unstructured, Outdoor | 7.1 GB | 26.4s | Hard | Rosbag |
Calibration | Office, Indoor | 1.3 GB | 9.3s | Rosbag |
@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}
}