DP-Habitat: Bridging the Gap Between Simulation and Reality for Visual Navigation in Dynamic Pedestrian Environments

Abstract

Visual navigation in dynamic environments poses a considerable challenge, particularly in scenarios with diverse pedestrian behaviors. Traditional simulators primarily focus on static scenes, while existing dynamic pedestrian simulators often suffer limitations such as monotonous pedestrian models, lack of interaction with the environment, and constrained scenarios. These deficiencies lead to notable discrepancies from real-world dynamic pedestrian environments. To bridge this gap, we introduce DP-Habitat, a dynamic pedestrian simulator developed on the Habitat platform. DP-Habitat efficiently simulates a wide range of complex and realistic human behaviors, with flexible interactions between pedestrian models and environments. It also supports rapid deployment of pedestrian models across various scenes, thereby more accurately replicating the complexities of real-world dynamic pedestrian settings. Additionally, we present Adaptive Object Navigation with Dynamic Mapping (AON-DM), a novel baseline method specifically designed for dynamic pedestrian settings. AON-DM integrates real-time pedestrian tracking and predictive modeling with a hybrid path planning strategy, markedly improving navigation efficiency and success rates. Our experimental results reveal that dynamic pedestrians significantly affect visual navigation performance within DP-Habitat, with AON-DM achieving superior effectiveness compared to existing methods under these challenging conditions. Furthermore, our approach maintains high performance in real-world scenarios, highlighting its practical applicability and robustness. The code and data are available at https://github.com/qinliangql/DP-Habitat.git.