Coarse-to-fine visual autonomous unmanned aerial vehicle landing on a moving platform

Abstract

Autonomous unmanned aerial vehicle (UAV) landing is a challenging task, especially on a moving platform in an unstructured environment. Under such a scenario, successful UAV landing is mainly affected by poor UAV localization performance. To solve this problem, we propose a coarse-to-fine visual autonomous UAV landing system based on an enhanced visual positioning approach. The landing platform is marked with a specially designed QR code marker, which is developed to improve the landing accuracy when the UAV approaches the landing site. Besides, we employ the you only look once framework to enhance the visual positioning accuracy, thereby promoting the landing platform detection when the UAV is flying far away. The framework recognizes the QR code and decodes the position of a UAV by the corner points of the QR code. Further, we use the Kalman filter to fuse the position data decoded from the QR code with those from the inertia measurement unit sensor. Then, the position data are used for UAV landing with a developed hierarchical landing strategy. To verify the effectiveness of the proposed system, we performed experiments in different environments under various light conditions. The experimental results demonstrate that the proposed system can achieve UAV landing with high accuracy, strong adaptability, and robustness. In addition, it can achieve accurate landing in different operating environments without external real-time kinematic global positioning system (RTK-GPS) signals, and the average landing error is 11.5 cm, which is similar to the landing error when using RTK-GPS signals as the ground truth.