面向GNSS拒止环境下鲁棒无人机跟踪：多LiDAR多无人机数据集
Towards Robust UAV Tracking in GNSS-Denied Environments: A Multi-LiDAR Multi-UAV Dataset
AI总结：
针对GNSS拒止环境，提出一个多LiDAR多无人机数据集，包含多种传感器和室内外场景，并基准测试现有算法，揭示当前方法在不同场景下无法一致跟踪的缺陷。
摘要：
随着无人机在各行各业的日益普及，在具有挑战性的环境中，特别是GNSS拒止区域，无人机的导航与跟踪已成为关键问题。为满足这一需求，我们提出一个专门用于无人机跟踪的新型多LiDAR数据集。我们的数据集包含来自一个旋转LiDAR、两个具有不同视场（FoV）和扫描模式的固态LiDAR，以及一个RGB-D相机的数据。这种多样化的传感器套件使得能够研究该领域的新挑战，包括有限视场适应性和多模态数据处理。该数据集有助于评估现有算法并开发新算法，为无人机跟踪技术的进步铺平道路。值得注意的是，我们提供了室内和室外环境的数据。我们还考虑了不同尺寸的无人机，从微型飞行器到更标准的商用无人机平台。室外轨迹选择在靠近建筑物的区域，针对城市区域无人机检测的研究，例如在反无人机系统或无人机物流对接中。除数据集外，我们还提供了与近期基于LiDAR的无人机跟踪算法的基线比较，对不同传感器、无人机和算法的性能进行基准测试。重要的是，我们的数据集表明，当前方法存在缺陷，无法在不同场景下一致地跟踪无人机。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
