Viewpoint-Aware Visibility Scoring for Point Cloud Registration in Loop Closure

Abstract

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.