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.

翻译：随着无人机在各行业的日益普及，在复杂环境（尤其是GNSS拒止区域）中实现无人机的导航与跟踪已成为关键问题。为应对这一需求，我们提出了一种专用于无人机跟踪的新型多激光雷达数据集。该数据集包含来自旋转式激光雷达、两种不同视场角（FoV）与扫描模式的固态激光雷达，以及RGB-D相机的数据。这种多样化的传感器套件可支持对领域新挑战的研究，包括有限视场角适应性及多模态数据处理。该数据集既便于评估现有算法，也促进新算法的开发，为无人机跟踪技术的进步铺平道路。值得注意的是，我们提供了室内与室外环境数据，并考虑了从微型飞行器到标准商用无人机平台等多种无人机尺寸。室外轨迹均选定于建筑物邻近区域，旨在支持城市环境中的无人机检测研究，例如反无人机系统或无人机物流对接。除数据集外，我们还提供了与近期基于激光雷达的无人机跟踪算法的基线对比，针对不同传感器、无人机及算法进行了性能基准测试。关键的是，我们的数据集表明，现有方法存在缺陷，无法在不同场景下持续稳定地跟踪无人机。