An Improved TrICP Point Cloud Registration Method Based on Automatically Trimming Overlap Regions

Abstract

To address the challenge of determining the parameters of registration algorithms under low point clouds overlap, which hinders automatic and efficient calculations, we introduce an improved variant of the TrICP algorithm capable of automatically extracting the overlap regions. Firstly, the triangle threshold method is used to estimate the distance threshold, and the overlap region is extracted and bidirectionally merged to obtain relatively complete overlap point clouds. To mitigate the risk of getting stuck in local optimal solutions and minimize the impact of incorrectly identified point pairs in non-overlap regions, we incorporate an effectiveness factor in the calculation of Singular Value Decomposition (SVD) to weight the importance of the point pairs. To decrease the overlap point clouds extraction times and reduce the associated time costs, we implemented multiple iterations following each extraction of the overlap point clouds. We compared our algorithm with the ICP and TrICP algorithms using publicly available point clouds data and demonstrated the effectiveness of our algorithm in automatically addressing the challenge of fine registration for point clouds with low overlap.