Generation of Indoor Open Street Maps for Robot Navigation from CAD Files

arXiv cs.RO / 4/1/2026

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Key Points

The paper proposes a fully automated pipeline that converts architectural CAD files into a hierarchical topometric OpenStreetMap (OSM)-style representation for indoor robot navigation.
It avoids reliance on SLAM by extracting structural layers from CAD, then using an AreaGraph-based topological segmentation to build a hierarchical graph of navigable spaces.
The resulting maps are enriched by automatically transferring textual labels from the CAD source and merging multiple floors into a unified, topologically consistent model.
The authors position the CAD-to-OSM approach as more scalable and robust for dynamic, large-scale indoor settings where SLAM-based maps can become obsolete and cause localization failures.
A user-friendly GUI and open-source code/dataset are provided to support practical adoption and benchmarking.

Abstract

The deployment of autonomous mobile robots is predicated on the availability of environmental maps, yet conventional generation via SLAM (Simultaneous Localization and Mapping) suffers from significant limitations in time, labor, and robustness, particularly in dynamic, large-scale indoor environments where map obsolescence can lead to critical localization failures. To address these challenges, this paper presents a complete and automated system for converting architectural Computer-Aided Design (CAD) files into a hierarchical topometric OpenStreetMap (OSM) representation, tailored for robust life-long robot navigation. Our core methodology involves a multi-stage pipeline that first isolates key structural layers from the raw CAD data and then employs an AreaGraph-based topological segmentation to partition the building layout into a hierarchical graph of navigable spaces. This process yields a comprehensive and semantically rich map, further enhanced by automatically associating textual labels from the CAD source and cohesively merging multiple building floors into a unified, topologically-correct model. By leveraging the permanent structural information inherent in CAD files, our system circumvents the inefficiencies and fragility of SLAM, offering a practical and scalable solution for deploying robots in complex indoor spaces. The software is encapsulated within an intuitive Graphical User Interface (GUI) to facilitate practical use. The code and dataset are available at https://github.com/jiajiezhang7/osmAG-from-cad.