BEV-OSP: Obstacle State Prediction in Bird's-Eye View to Enable Obstacle Avoidance and Navigation in Dynamic Environments

Abstract

Despite the prevalence of deep reinforcement learning (DRL) for navigation in dynamic environments, existing end-to-end DRL methods still struggle to effectively balance global planning with local obstacle avoidance and to perceive the dynamics of obstacles. Meanwhile, external detection methods are also often susceptible to noise and limited in accuracy. To address this, we propose an efficient perception framework, Obstacle State Prediction in Bird’s-Eye View (BEV-OSP), which generates an intermediate Bird’s-Eye View (BEV) feature by fusing sensor data with robot velocity. This representation filters out irrelevant sensory noise and static background details without relying on any third-party detection module. We further integrate a self-supervised auxiliary task based on Bootstrap Your Own Latent (BYOL) contrastive learning into the DRL framework to predict obstacle states in latent space, which boosts the encoder’s perception of environmental dynamics. Finally, to ensure continuous navigation, a subgoal prediction module that can generate waypoints when the global planner is unavailable is introduced. Extensive experiments including successful real-world deployments in various scenarios demonstrate that BEV-OSP outperforms state-of-the-art (SOTA) methods in evaluations across multiple navigation metrics.