Pairwise Point Cloud Registration using Graph Matching and Rotation-invariant Features

TL;DR

This paper introduces a novel coarse-to-fine point cloud registration method that employs rotation-invariant features and graph matching to improve correspondence accuracy, achieving high precision in benchmark tests.

Contribution

It presents a new registration strategy combining rotation-invariant features with a weighted graph matching approach for improved point cloud alignment.

Findings

Achieves rotation errors less than 0.2 degrees.

Attains translation errors under 0.1 meters.

Outperforms several state-of-the-art methods on benchmark datasets.

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 and a new weighted graph matching method for iteratively finding correspondence. In the graph matching method, the similarity of nodes and edges in Euclidean and feature space are 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 degrees and translation errors of less than 0.1m.