OPEN: Lightweight Map-Based Semantic Navigation for GPS-Free Last-Mile Delivery

The growing demand for efficient last-mile delivery highlights the need for autonomous robots to improve operational efficiency and reduce costs. Traditional navigation methods rely on high-precision maps, which are expensive to produce and maintain, while learning-based approaches often struggle to adapt to diverse real-world environments. To address these challenges, this paper presents OpenStreetMap-enhanced oPen-air sEmantic Navigation (OPEN), a system that combines foundation models with classic navigation algorithms to enable scalable outdoor navigation. By leveraging off-the-shelf OpenStreetMap (OSM), OPEN eliminates the need for extensive pre-mapping and provides a lightweight, readily available map representation. The system uses Large Language Models (LLMs) to interpret delivery instructions and Vision Language Models (VLMs) for global localization without relying on GPS, real-time map updates, and entrance recognition, ensuring robust navigation in complex environments. To further enhance adaptability, OPEN incorporates a local replanning method that dynamically adjusts waypoints in response to environmental changes and OSM inaccuracies. Since existing benchmarks do not adequately reflect the challenges of last-mile delivery, this work introduces a new benchmark designed for residential navigation. Experiments conducted in both simulated and real-world settings demonstrate that OPEN improves navigation accuracy, efficiency, and reliability, outperforming existing semantic navigation methods. To facilitate further research, the code and benchmark are publicly available.