Graph Matching for Underwater Simultaneous Localization and Mapping Using Multibeam Sonar Imaging

Abstract

This paper addresses the challenges of underwater Simultaneous Localization and Mapping (SLAM) using multibeam sonar imaging. The widely used Iterative Closest Point (ICP) often falls into local optima due to non-convexity and the lack of features for correct registration. To overcome this, we propose a novel registration algorithm based on Gaussian clustering and Graph Matching with maximal cliques. The proposed approach enhances feature-matching accuracy and robustness in complex underwater environments. Inertial measurements and velocity estimates are also fused for global state estimation. Comprehensive tests in simulated and real-world underwater environments have demonstrated that the proposed registration method effectively addresses the issue of the ICP algorithm easily falling into local optima while also exhibiting excellent inter-frame registration performance and robustness.