A Marker-Based Method of Autonomous Landing for Unmanned Aerial Vehicles
Ruilan Yang, Yuhua Qi, Chong Pan, Xiong Li, Hongbo Chen · 2024
In recent years, unmanned aerial vehicles (UAVs) have undergone rapid development and are widely utilized in fields such as rescue and goods delivery. Autonomous landing is a key problem for UAVs in these scenarios and this often relies on Global Navigation Satellite System (GNSS) or Real Time Kinematic (RTK). However, these systems require relatively expensive equipment and well conditions for GNSS signals. To achieve precise autonomous landing in the GNSS denied environment, a novel landing pad and pose estimation method based on ArUco markers are designed. We propose a new marker arrangement to facilitate accurate recognition at different distances, and also design an outlier removal method to integrate the results from multiple markers. We evaluate the localization accuracy of our method through various experiments and compare it with another vision-based method. The results indicate that our method improves the landing accuracy of UAVs while maintaining a low computational demand.