SLAM2REF: Advancing Long-Term Mapping with 3D LiDAR and Reference Map Integration for Precise 6-DoF Trajectory Estimation and Map Extension
<|reference_start|>SLAM2REF: Advancing Long-Term Mapping with 3D LiDAR and Reference Map Integration for Precise 6-DoF Trajectory Estimation and Map Extension: This paper presents a pioneering solution to the task of integrating mobile 3D LiDAR and inertial measurement unit (IMU) data with existing building information models or point clouds, which is crucial for achieving precise long-term localization and mapping in indoor, GPS-denied environments. Our proposed framework, SLAM2REF, introduces a novel approach for automatic alignment and map extension utilizing reference 3D maps. The methodology is supported by a sophisticated multi-session anchoring technique, which integrates novel descriptors and registration methodologies. Real-world experiments reveal the framework's remarkable robustness and accuracy, surpassing current state-of-the-art methods. Our open-source framework's significance lies in its contribution to resilient map data management, enhancing processes across diverse sectors such as construction site monitoring, emergency response, disaster management, and others, where fast-updated digital 3D maps contribute to better decision-making and productivity. Moreover, it offers advancements in localization and mapping research. Link to the repository: https://github.com/MigVega/SLAM2REF, Data: https://doi.org/10.14459/2024mp1743877.<|reference_end|>
@article{torres2024slam2ref:,
    title={SLAM2REF: Advancing Long-Term Mapping with 3D LiDAR and Reference Map
  Integration for Precise 6-DoF Trajectory Estimation and Map Extension},
    author={Miguel Arturo Vega Torres, Alexander Braun, Andr'e Borrmann},
    journal={Journal of Construction Robotics, Springer (2024)},
    year={2024},
    doi={10.1007/s41693-024-00126-w},
    archivePrefix={arXiv},
    eprint={2408.15948},
    primaryClass={cs.RO}
}