A Heterogeneous UAV UGV Methodology for Aerial Tracking of Mini Autonomous Cars

Aerial tracking of autonomous ground vehicles can provide useful situational awareness in environments where fixed external perception infrastructure is unavailable. However, visual-only tracking from an unmanned aerial vehicle (UAV) is limited by camera field of view, viewpoint changes, occlusions, and temporary loss of target visibility. This paper presents a heterogeneous UAV--UGV methodology for aerial tracking of small-scale autonomous ground vehicles in simulation. The system is implemented in ROS 2 and Gazebo Sim using a Tello-like UAV equipped with a front-facing RGB camera and multiple LIMO-like unmanned ground vehicles (UGVs). Three approaches are evaluated: a training-free color-marker baseline, a YOLO-based visual baseline, and a proposed UAV--UGV fusion method. The proposed method uses the same visual detector as the YOLO baseline, but combines visual detections with UGV state information to maintain the target estimate during visual gaps. The methods are evaluated across four scenarios: single-car tracking, crossing trajectories, occlusion, and UAV-assisted awareness. Results over 600 simulation runs show that the proposed fusion method achieves nearly continuous track coverage across all scenarios while reducing localization error relative to the YOLO baseline. Compared with the visual-only YOLO approach, the fusion method reduces RMSE by 37.3%, 64.2%, 62.6%, and 61.8% in the four evaluated scenarios, respectively. The method also enables conflict-awareness lead times of 10.20 s in the crossing scenario and 9.07 s in the UAV-assisted awareness scenario. These results indicate that heterogeneous UAV--UGV fusion can improve aerial tracking continuity and robustness when visual observations are intermittent.