Abstract
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.
Abstract (translated)
建筑和机器人感测数据来自不同的来源，并与独特的视角相关。本研究的主要目标是用全局点云配准方法将激光雷达点云与建筑信息模型（BIM）对齐，旨在建立两种数据之间的共同理解，即“使用相同的语言交流”。为实现这一目标，我们设计了一种跨模态配准方法，从前端到后端。在前端，我们通过识别墙并捕获相交角来提取描述符。接下来，为了后端姿态估计，我们使用Hough变换来进行姿态估计，估计多个姿态候选者。最后，通过墙像素相关性验证最终姿态。为了评估我们方法的有效性，我们在一个大型的大学楼中进行了多次现实世界的多会话实验，涉及两种不同类型的激光雷达传感器。我们还报告了我们的发现，并计划将我们所收集的数据公开开源。
URL
https://arxiv.org/abs/2405.03969
