osmAG-Nav：一种用于鲁棒长期室内自主性的分层语义拓扑导航栈
osmAG-Nav: A Hierarchical Semantic Topometric Navigation Stack for Robust Lifelong Indoor Autonomy
AI总结：
本文提出osmAG-Nav导航栈，通过分层语义拓扑地图实现大空间多层环境中的高效导航，降低规划延迟并提升长期定位稳定性。
摘要：
在大规模多层环境中部署移动机器人需要能够实现空间扩展性而不牺牲局部运动学精度的导航系统。传统导航栈依赖于单一占用网格地图，面临存储效率、跨楼层推理和长距离规划的严重瓶颈。为此，本文提出osmAG-Nav，一种基于分层语义拓扑的开放源代码ROS2导航栈。该系统采用
英文摘要：
The deployment of mobile robots in large-scale, multi-floor environments demands navigation systems that achieve spatial scalability without compromising local kinematic precision. Traditional navigation stacks, reliant on monolithic occupancy grid maps, face severe bottlenecks in storage efficiency, cross-floor reasoning, and long-horizon planning. To address these limitations, this paper presents osmAG-Nav, a complete, open-source ROS2 navigation stack built upon the hierarchical semantic topometric OpenStreetMap Area Graph (osmAG) map standard. The system follows a "System of Systems" architecture that decouples global topological reasoning from local metric execution. A Hierarchical osmAG planner replaces dense grid searches with an LCA-anchored pipeline on a passage-centric graph whose edge costs derive from local raster traversability rather than Euclidean distance, yielding low-millisecond planning on long campus-scale routes. A Rolling Window mechanism rasterizes a fixed-size local metric grid around the robot, keeping the local costmap memory footprint independent of the total mapped area, while a Segmented Execution strategy dispatches intermediate goals to standard ROS2 controllers for smooth handoffs. System robustness is reinforced by a structure-aware LiDAR localization framework that filters dynamic clutter against permanent architectural priors. Extensive experiments on a real-world multi-story indoor-outdoor campus (>11,025 m^2) show that, on the same-floor benchmark subset, osmAG-Nav delivers up to 7816x lower planning latency than a grid-based baseline on long routes while maintaining low path-length overhead and lifelong localization stability. A single-floor long-range robot mission further validates the integrated stack reliability. The full stack is released as modular ROS2 Lifecycle Nodes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
