BIM-SLAM：使用3D LiDAR将BIM模型集成到多会话SLAM中以实现终身建图
BIM-SLAM: Integrating BIM Models in Multi-session SLAM for Lifelong Mapping using 3D LiDAR
AI总结：
本文提出BIM-SLAM方法，利用3D LiDAR测量和BIM模型生成会话数据，通过多会话锚定对齐真实数据以最小化漂移，并重建模型中缺失的新元素，实现了无需初始位姿先验的室内终身建图。
摘要：
随着3D LiDAR传感器技术日益先进且成本不断降低，AEC行业数字化的发展使得大部分建筑环境已具备3D建筑信息模型（BIM模型）。这两点引出了一个问题：在无GPS的室内环境中，3D模型如何支持3D LiDAR长期SLAM。本文提出了一种方法，利用BIM模型结合连续的LiDAR测量来创建室内环境的更新地图。会话数据（基于位姿图的地图和描述子）最初由BIM模型生成。随后，利用多会话锚定将真实世界数据与模型生成的会话数据进行对齐，同时最小化真实世界数据的漂移。最后，识别BIM模型中不存在的新元素，对其进行分组并以表面表示进行重建，从而在BIM模型旁实现更好的可视化。该框架能够创建与BIM模型对齐的连贯地图，且无需预先知道机器人的初始位姿，也不要求机器人必须位于地图内部。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
