Graph Correspondence-Based Point Set Registration

Point set registration, crucial in computer vision and robotics applications, encounters challenges, such as noise, outliers, and misalignment. Current methods often struggle with these issues, leading to suboptimal registration accuracy. This article proposes a novel graph correspondence-based algorithm to address these challenges in rigid point set registration. We model point sets as graphs, transforming the registration problem into a graph isomorphism problem. This approach is enhanced with probabilistic linear programming heuristics to efficiently establish correspondences between point sets. Our method significantly improves robustness against common registration errors and does not require initial pose estimation, a notable advantage over existing algorithms. Extensive experiments on various datasets, including applications in intelligent vehicle mapping and localization, demonstrate superior performance in correspondence establishment and registration accuracy compared to state-of-the-art methods, particularly under conditions of noise, outliers, and misalignment.