osmAG-Nav: A Hierarchical Semantic Topometric Navigation Stack for Robust Lifelong Indoor Autonomy

2026-03-30

AI Summary

This work addresses the limitations of conventional single-layer occupancy grid maps in large-scale multi-floor environments, which suffer from high memory overhead, difficulty in cross-floor reasoning, and inefficient long-horizon planning. The authors propose a ROS2-based navigation system built upon a hierarchical semantic topometric map named osmAG, employing a "system-of-systems" architecture that decouples global topological planning from local metric execution. Key innovations include an aisle-centric osmAG graph structure, lowest common ancestor (LCA)-anchored path planning, a rolling-window local costmap, a segmented goal execution mechanism, and LiDAR-based localization enhanced with structural priors. Evaluated in a real-world multi-floor setting exceeding 11,000 square meters, the approach reduces planning latency for intra-floor long-distance tasks by up to 7,816× compared to traditional methods while preserving path optimality and long-term localization robustness.

Abstract

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.

Problem

• indoor navigation
• spatial scalability
• multi-floor environments
• lifelong autonomy
• occupancy grid maps

Innovation

• hierarchical semantic topometric navigation
• OpenStreetMap Area Graph (osmAG)
• LCA-anchored planning
• rolling window local costmap
• structure-aware LiDAR localization