From Anywhere to the Destination: Visual Reinforcement Learning for GNSS-Denied Drone Navigation

Unmanned aerial vehicles (UAVs), also referred to as drones, typically depend on the Global Navigation Satellite System (GNSS) for positional awareness and navigation. However, the GNSS can be unreliable in specific environments due to signal obstruction or degradation. In recent years, vision-based navigation has emerged as a promising alternative to GNSS. Despite its potential, this visual method necessitates a significant investment in expert-guided demonstrations, resulting in a cumbersome and passive collection process, especially for large-scale outdoor navigation tasks. In this paper, we propose AceFormer, a vision-based active exploration transformer framework that enables agent navigation from any starting point to a destination without expert guidance. Specifically, this framework introduces an intra instance feature encoder in tandem with a semantic guidance decoder, which empowers the drone agent to extract global instance-level semantic information. Deep reinforcement learning is further employed to train the drone agent, enabling it to explore the environment dynamically, and learn navigation tasks in a trial-and-error way. To empirically evaluate AceFormer, a challenging drone navigation simulated flight environment (DroEnv) is designed by leveraging Google Earth as a navigation platform, which contributes to a standardized benchmark for the drone navigation field. Extensive experiments conducted on DroEnv with randomly sampled starting points demonstrate the effectiveness of the proposed AceFormer in both ideal and noisy environments. Our code is publicly available.