Semantic enrichment of BIM with IndoorGML for quadruped robot navigation and automated 3D scanning

Planning scan routes with prior knowledge can improve scan data quality and completeness. This paper presents a BIM-enabled approach to optimize quadruped robot navigation for automated 3D scanning. The BIM data schema is enriched with IndoorGML, integrating building geometry with spatial data to establish an indoor navigation model describing multi-scale spatial topological networks. This navigation model, which includes an enhanced greedy algorithm, optimizes quadruped robot scanning positions and traversal sequences. The scan planning optimization outperforms existing heuristic algorithms in computational efficiency, coverage, and scan point count. The BIM-enabled approach is validated on ROS and in real-world conditions with a 3D LiDAR sensor integrated with a quadruped robot. The robotic scans achieve visible coverage of 70 - 90% of the structure, with a fluctuation of 0.006 - 0.021 mm compared to traditional laser scans. The findings demonstrate robotic scans as a viable way of obtaining complete and accurate point clouds, reducing human effort in traditional scanning.