UAV tracking and pose estimation plays an imperative role in various UAV-related missions, such as formation control and anti-UAV measures. Accurately detecting and tracking UAVs in a 3D space remains a particularly challenging problem, as it requires extracting sparse features of micro UAVs from different flight environments and continuously matching correspondences, especially during agile flight. Generally, cameras and LiDARs are the two main types of sensors used to capture UAV trajectories in flight. However, both sensors have limitations in UAV classification and pose estimation. This technical report briefly introduces the method proposed by our team "NTU-ICG" for the CVPR 2024 UG2+ Challenge Track 5. This work develops a clustering-based learning detection approach, CL-Det, for UAV tracking and pose estimation using two types of LiDARs, namely Livox Avia and LiDAR 360. We combine the information from the two data sources to locate drones in 3D. We first align the timestamps of Livox Avia data and LiDAR 360 data and then separate the point cloud of objects of interest (OOIs) from the environment. The point cloud of OOIs is clustered using the DBSCAN method, with the midpoint of the largest cluster assumed to be the UAV position. Furthermore, we utilize historical estimations to fill in missing data. The proposed method shows competitive pose estimation performance and ranks 5th on the final leaderboard of the CVPR 2024 UG2+ Challenge.

翻译：无人机跟踪与姿态估计在编队控制、反无人机措施等多种无人机相关任务中具有至关重要的作用。在三维空间中精确检测与跟踪无人机仍是一个极具挑战性的问题，因为这需要从不同的飞行环境中提取微型无人机的稀疏特征并持续匹配对应关系，尤其在无人机进行敏捷飞行时更为困难。通常，相机与激光雷达是用于捕获飞行中无人机轨迹的两类主要传感器。然而，这两种传感器在无人机分类与姿态估计方面均存在局限性。本技术报告简要介绍了我们团队“NTU-ICG”为CVPR 2024 UG2+挑战赛第五赛道所提出的方法。本研究开发了一种基于聚类的学习检测方法CL-Det，利用Livox Avia与LiDAR 360两种激光雷达实现无人机跟踪与姿态估计。我们融合两种数据源的信息以定位三维空间中的无人机。首先对齐Livox Avia数据与LiDAR 360数据的时间戳，随后从环境中分离出感兴趣对象的点云。使用DBSCAN方法对感兴趣对象的点云进行聚类，并假设最大聚类的中点即为无人机位置。此外，我们利用历史估计值填补缺失数据。所提方法在姿态估计方面表现出竞争力，在CVPR 2024 UG2+挑战赛的最终排行榜上位列第五。