The construction and robotic sensing data originate from disparate sources and are associated with distinct frames of reference. The primary objective of this study is to align LiDAR point clouds with building information modeling (BIM) using a global point cloud registration approach, aimed at establishing a shared understanding between the two modalities, i.e., ``speak the same language''. To achieve this, we design a cross-modality registration method, spanning from front end the back end. At the front end, we extract descriptors by identifying walls and capturing the intersected corners. Subsequently, for the back-end pose estimation, we employ the Hough transform for pose estimation and estimate multiple pose candidates. The final pose is verified by wall-pixel correlation. To evaluate the effectiveness of our method, we conducted real-world multi-session experiments in a large-scale university building, involving two different types of LiDAR sensors. We also report our findings and plan to make our collected dataset open-sourced.

翻译：建筑与机器人感知数据源自不同来源，且关联于各自独立的参考坐标系。本研究的主要目标是通过全局点云配准方法，将LiDAR点云与建筑信息模型（BIM）对齐，旨在建立两种模态间的共享理解，即实现“说同一种语言”。为此，我们设计了一种跨模态配准方法，涵盖前端与后端处理。在前端，我们通过识别墙体并提取相交角点来生成描述子；在后端位姿估计中，我们采用霍夫变换进行位姿估计，并生成多个候选位姿。最终位姿通过墙像素相关性进行验证。为评估方法的有效性，我们在大型大学建筑中开展了多会话真实场景实验，使用了两种不同类型的LiDAR传感器。我们还报告了研究发现，并计划将采集的数据集开源。