TY - GEN
T1 - Air-to-Air Detection and Tracking of Non-Cooperative UAVs for 3D Reconstruction
AU - Liu, Yang
AU - Xi, Lele
AU - Sun, Zhihao
AU - Zhang, Lele
AU - Dong, Wei
AU - Lu, Maobin
AU - Chen, Chen
AU - Deng, Fang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The monitoring and regulation of unauthorized, unresponsive, or uncommunicative non-cooperative UAVs is still one of the great challenges in airspace safety management. Therefore, in order to obtain the phenotypic information of these targets, we propose a 3D reconstruction system for non-cooperative UAVs based on air-to-air view image sequences. Specifically, the data-collecting UAV (pursuer) will detect, track, approach, and follow the non-cooperative UAV (target) at a fixed distance to automatically capture the image data. During the process, a lightweight object detection model and a Kalman filter-based tracking algorithm are used to recognize and continuously track the target UAV, and the boundary state constrained primitives and interval replanning are used to generate the accurate flight path, which in turn will control the pursuer UAV to approach the target UAV and follow it at a fixed distance. Finally, based on the implicit representation of the neural radiation field, the collected images will be used for 3D reconstruction and rendering of the non-cooperative UAV. In experiments, taking a quadrotor as the pursuer and a fixed-wing UAV as the target, we verify the feasibility of the proposed system in 3D reconstruction tasks.
AB - The monitoring and regulation of unauthorized, unresponsive, or uncommunicative non-cooperative UAVs is still one of the great challenges in airspace safety management. Therefore, in order to obtain the phenotypic information of these targets, we propose a 3D reconstruction system for non-cooperative UAVs based on air-to-air view image sequences. Specifically, the data-collecting UAV (pursuer) will detect, track, approach, and follow the non-cooperative UAV (target) at a fixed distance to automatically capture the image data. During the process, a lightweight object detection model and a Kalman filter-based tracking algorithm are used to recognize and continuously track the target UAV, and the boundary state constrained primitives and interval replanning are used to generate the accurate flight path, which in turn will control the pursuer UAV to approach the target UAV and follow it at a fixed distance. Finally, based on the implicit representation of the neural radiation field, the collected images will be used for 3D reconstruction and rendering of the non-cooperative UAV. In experiments, taking a quadrotor as the pursuer and a fixed-wing UAV as the target, we verify the feasibility of the proposed system in 3D reconstruction tasks.
UR - http://www.scopus.com/inward/record.url?scp=85200352360&partnerID=8YFLogxK
U2 - 10.1109/ICCA62789.2024.10591888
DO - 10.1109/ICCA62789.2024.10591888
M3 - Conference contribution
AN - SCOPUS:85200352360
T3 - IEEE International Conference on Control and Automation, ICCA
SP - 936
EP - 941
BT - 2024 IEEE 18th International Conference on Control and Automation, ICCA 2024
PB - IEEE Computer Society
T2 - 18th IEEE International Conference on Control and Automation, ICCA 2024
Y2 - 18 June 2024 through 21 June 2024
ER -