Pairwise point cloud registration using graph matching and rotation-invariant features

Abstract

Registration is a fundamental but critical task in point cloud processing, which usually depends on finding element correspondence from two point clouds. However, the finding of reliable correspondence relies on establishing a robust and discriminative description of elements and the correct matching of corresponding elements. In this letter, we develop a coarse-to-fine registration strategy, which utilizes rotation-invariant features in frequency domain and a new graph matching (GM) method for iteratively searching correspondence. In the GM method, the similarity of both nodes and edges in the Euclidean and feature space is formulated to construct the optimization function. The proposed strategy is evaluated using two benchmark datasets and compared with several state-of-the-art methods. Regarding the experimental results, our proposed method can achieve a fine registration with rotation errors of less than 0.2° and translation errors of less than 0.1 m.