MULoc: Robust Monocular Visual Localization for Underwater Robots via Multi-modal Feature Fusion*

Precise state estimation and localization for underwater robots are essential prerequisites for autonomous operation in complex aquatic environments. While traditional underwater localization methods primarily rely on specialized hardware such as acoustic sensors and inertial measurement units, these approaches often increase system complexity, cost, and potential failure points. This research presents MULoc (Multi-feature Underwater Localization), a real-time monocular visual localization framework for underwater robots that leverages complementary feature fusion to overcome the significant challenges presented by underwater environments, including non-uniform illumination, scattering effects, and varying turbidity levels. By utilizing a single camera approach, MULoc significantly reduces hardware complexity and cost while maintaining robust performance. The proposed framework implements an end-to-end optimization mechanism that integrates deep learning-based RGB feature extraction with geometric depth estimation derived from the same monocular source. Experimental validation in controlled underwater testing facilities demonstrates that MULoc achieves an average localization error below 1% of trajectory length across diverse underwater scenarios, with real-time processing capabilities at 25 FPS on embedded computing platforms. Performance comparisons reveal substantial improvements in localization performance compared to traditional methods and a tracking success rate of 92% in challenging conditions. The presented system offers a comprehensive and cost-effective automation solution for underwater robot navigation with significant applications in marine resource exploration, infrastructure inspection, and underwater archaeology.