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

2025-07-01

AI Summary

To address the challenges of time-consuming, fragile, and rapidly outdated SLAM mapping in dynamic large-scale indoor environments—coupled with heavy reliance on manual intervention—this paper proposes a CAD-based automated method for constructing hierarchical topological navigation maps. Our approach introduces an AreaGraph-driven semantic topological segmentation framework that enables structured parsing of multi-floor CAD data, layer-level semantic recognition, automatic association of textual labels, and cross-floor topological fusion. The system generates unified, topologically consistent, and indoor-outdoor aligned navigation maps in OpenStreetMap format via a graphical workflow. This significantly improves map freshness and enhances long-term robotic deployment robustness. We open-source both code and datasets, and experimental validation demonstrates high efficiency and strong generalization across complex building scenarios.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Convert CAD files to robot-navigable OpenStreetMap automatically
• Overcome SLAM limitations in dynamic indoor environments
• Generate hierarchical topometric maps for lifelong navigation

Innovation

• Automated CAD to OSM conversion system
• Hierarchical topometric map generation
• GUI-integrated scalable solution