Implicit Neural Mapping for a Data Closed-Loop Unmanned Aerial Vehicle Pose-Estimation Algorithm in a Vision-Only Landing System

Abstract

Due to their low cost, interference resistance, and concealment of vision sensors, vision-based landing systems have received a lot of research attention. However, vision sensors are only used as auxiliary components in visual landing systems because of their limited accuracy. To solve the problem of the inaccurate position estimation of vision-only sensors during landing, a novel data closed-loop pose-estimation algorithm with an implicit neural map is proposed. First, we propose a method with which to estimate the UAV pose based on the runway’s line features, using a flexible coarse-to-fine runway-line-detection method. Then, we propose a mapping and localization method based on the neural radiance field (NeRF), which provides continuous representation and can correct the initial estimated pose well. Finally, we develop a closed-loop data annotation system based on a high-fidelity implicit map, which can significantly improve annotation efficiency. The experimental results show that our proposed algorithm performs well in various scenarios and achieves state-of-the-art accuracy in pose estimation.