LiPMatch: LiDAR Point Cloud Plane Based Loop-Closure

Abstract

This letter presents a point cloud based loop-closure method to correct long-term drifts in Light Detection and Ranging based Simultaneous Localization and Mapping systems. In the method, we formulate each keyframe as a fully-connected graph with nodes representing planes. To detect loop-closures, the proposed method employs geometric restrictions to define a similarity metric to match current keyframe and those in the map. After similarity assessment, the candidate keyframes which comply with the geometric restrictions are further checked out successively by normal constraints of planes, and validated by an improved Iterative Closest Point method. The latter also provides relative pose transformation estimation between the current keyframe and the matched keyframe in the global reference frame. Experimental results demonstrate that the proposed method is able to fulfill fast and reliable loop-closure. To benefit the community by serving a benchmark for loop-closure, the entire system is made open source on GitHub.