A Global Optimal and Outlier-Robust Point Set Registration Method

Point set registration is an essential technique in the field of machine vision. In this article, we propose a robust global optimal solution to for the point set registration of feature points extracted from visual images, used in remote (300–120 km) space target tracking and targeting tasks. Specifically, we begin with cases where correspondences among point sets are known, establishing a cost function centered on maximizing the consensus set, wherein rotational and translational parameters are determined using voting methods and the branch-and-bound (BnB) algorithm, respectively. We then adapt this foundation to tackle the more challenging scenario of unknown correspondences in simultaneous pose and correspondence registration by adjusting the cost function and BnB bounding functions, supplemented with nested iterations to accurately determine rotation and translation parameters. Finally, the comprehensive experimental comparisons executed across synthetic and real datasets, along with ground-based spacecraft pose measurement setup, illustrate that, compared to existing methods, our proposed approach achieves precise estimations under the influence of noise and outliers. Moreover, compared to the globally nested BnB scheme, our method reduces computational complexity and enhances solution speeds.