Stereo Vision Guiding for the Autonomous Landing of Fixed-Wing UAVs: A Saliency-Inspired Approach

Abstract

It is an important criterion for unmanned aerial vehicles (UAVs) to land on the runway safely. This paper concentrates on stereo vision localization of a fixed-wing UAV's autonomous landing within global navigation satellite system (GNSS) denied environments. A ground stereo vision guidance system imitating the human visual system (HVS) is presented for the autonomous landing of fixed-wing UAVs. A saliency-inspired algorithm is presented and developed to detect flying UAV targets in captured sequential images. Furthermore, an extended Kalman filter (EKF) based state estimation is employed to reduce localization errors caused by measurement errors of object detection and pan-tilt unit (PTU) attitudes. Finally, stereo-vision-dataset-based experiments are conducted to verify the effectiveness of the proposed visual detection method and error correction algorithm. The compared results between the visual guidance approach and differential GPS-based approach indicate that the stereo vision system and detection method can achieve the better guiding effect.