Vision-based autonomous UGV detection, tracking, and following for a UAV - Amil, Fatma G.; Sen, Muhammet A.; Kurt, Huseyin Burak; Beycimen, Semih; Millidere, Murat (American Institute of Aeronautics and Astronautics (AIAA), 2024)
This study proposes a methodology for unmanned ground vehicle (UGV) navigation in off-road environments where GPS signals are not available. The Husky-A200 at Cranfield University, United Kingdom has been used as a UGV in this research project. Due to the limited field of vision of UGVs, a UAV-UGV collaboration approach was adopted. The methodology involves five steps. The first step is divided into three phases: The aerial images of UGV from UAV are generated in the first phase. In the second phase, the UGV is detected and tracked using computer vision techniques. In the third phase, the relative pose (position and heading) between the UAV and UGV is estimated continuously using visual data. In the second step, the UAV maintain a fixed location (position and heading) relative to the UGV. The third step involves capturing aerial images from the UAV‘s mounted camera and transmitting it to the ground station instantly to create a global traversability map that classifies terrain features based on their traversability. In the fourth step, additional sensors such as LiDAR, radar, and IMU are used to refine the global traversability map. In the final step, the UGV navigates automatically using the refined traversability map. This study will focus on the first two steps of the methodology, while subsequent studies will address the remaining steps.
Keywords: Unmanned Aerial Vehicle; Unmanned Ground Vehicle; Computer Vision; Onboard Sensors; Track Algorithm; LIDAR; Ground Station; Yaw Control; Proportional Integral Derivative; Deep Learning