LiDAR-based Simultaneous Localization and Mapping (SLAM) encounters a substantial challenge in the form of accumulating errors, which can adversely impact its reliability. Loop closing techniques have been extensively employed to counteract this issue. Nonetheless, the loop closing conundrum remains difficult to resolve, as point clouds often exhibit partial overlap due to disparities in scanning pose (viewpoints). This renders the conventional point cloud registration such as Iterative Closest Point (ICP) algorithm problematic. To overcome this challenge, this paper proposes a two-stage viewpoint-aware point cloud registration technique that assigns suitable weights to the correspondence pairs associating two point clouds from different viewpoints. The weights account for the visibility of points from their respective viewpoint as well as from the viewpoint of the counterpart point cloud, making the registration more relying on commonly visible points from the both viewpoints. Experimental results, utilizing the KITTI and Apollo-SouthBay datasets, indicate that the proposed technique delivers more precise and robust performance compared to the baseline techniques.

中文翻译：

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循环闭合中点云注册的视点感知可见性评分

基于 LiDAR 的同步定位与建图 (SLAM) 遇到了累积错误形式的重大挑战，这可能对其可靠性产生不利影响。闭环技术已被广泛采用来解决这个问题。尽管如此，闭环难题仍然难以解决，因为由于扫描姿势（视点）的差异，点云经常表现出部分重叠。这使得传统的点云配准（例如迭代最近点（ICP）算法）出现问题。为了克服这一挑战，本文提出了一种两阶段视点感知点云配准技术，该技术为关联来自不同视点的两个点云的对应对分配合适的权重。权重考虑了点从各自的视点以及对应点云的视点的可见性，使得配准更加依赖于两个视点的共同可见点。利用 KITTI 和 Apollo-SouthBay 数据集的实验结果表明，与基线技术相比，所提出的技术提供了更精确、更稳健的性能。