BIM-SLAM: Integrating BIM Models in Multi-session SLAM for Lifelong Mapping using 3D LiDAR
<|reference_start|>BIM-SLAM: Integrating BIM Models in Multi-session SLAM for Lifelong Mapping using 3D LiDAR: While 3D LiDAR sensor technology is becoming more advanced and cheaper every day, the growth of digitalization in the AEC industry contributes to the fact that 3D building information models (BIM models) are now available for a large part of the built environment. These two facts open the question of how 3D models can support 3D LiDAR long-term SLAM in indoor, GPS-denied environments. This paper proposes a methodology that leverages BIM models to create an updated map of indoor environments with sequential LiDAR measurements. Session data (pose graph-based map and descriptors) are initially generated from BIM models. Then, real-world data is aligned with the session data from the model using multi-session anchoring while minimizing the drift on the real-world data. Finally, the new elements not present in the BIM model are identified, grouped, and reconstructed in a surface representation, allowing a better visualization next to the BIM model. The framework enables the creation of a coherent map aligned with the BIM model that does not require prior knowledge of the initial pose of the robot, and it does not need to be inside the map.<|reference_end|>
@article{torres2024bim-slam:,
    title={BIM-SLAM: Integrating BIM Models in Multi-session SLAM for Lifelong
  Mapping using 3D LiDAR},
    author={Miguel Arturo Vega Torres, Alexander Braun, Andr'e Borrmann},
    journal={Pages 521-528 (2023 Proceedings of the 40th ISARC, Chennai, India,
  ISBN 978-0-6458322-0-4, ISSN 2413-5844)},
    year={2024},
    doi={10.22260/ISARC2023/0070},
    archivePrefix={arXiv},
    eprint={2408.15870},
    primaryClass={cs.RO}
}