  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.
