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This paper introduces a Contact-Aided Factor-Graph Localization framework designed to enhance state estimation for autonomous underwater vehicles during close-range seafloor sampling. By integrating suction-based manipulator contact events with adaptive visual odometry and onboard sensor data, the method effectively mitigates issues such as scale ambiguity and trajectory drift in featureless environments. Experimental results reveal that this approach significantly improves object revisit accuracy and reduces trajectory drift compared to traditional filtering-based navigation methods.
Contact-induced constraints can dramatically enhance localization accuracy in underwater environments plagued by visual ambiguity and drift.
Accurate state estimation for autonomous underwater vehicles performing close-range seafloor sampling remains challenging. In low-altitude operation, down-looking cameras over featureless planar seabeds produce scale ambiguity, lateral degeneracy, and inconsistent feature tracking. Meanwhile, inertial-Doppler Velocity Log (DVL) fusion alone provides no mechanism for structural drift correction. We propose a Contact-Aided Factor-Graph Localization framework that treats physical interaction as an informative geometric constraint within a smoothing-based localization formulation. The method tightly fuses suction-based manipulator contact events with adaptive visual odometry, learned object detections, and on-board sensors. Visual odometry relative-pose factors and landmark bearing-range factors are uncertainty-scaled according to inlier statistics to prevent visually weak frames from destabilizing the estimator, while contact events are modeled as high-confidence factors that induce implicit loop closures without appearance-based place recognition. Furthermore, the system can fully initialize online during motion. Experimental evaluation in tanks, harbor, and simulation environments demonstrates that contact-induced constraints significantly reduce trajectory drift and improve object revisit accuracy compared to filtering-based navigation and contact-free graph formulations. These results highlight the role of embodied physical interaction as a localization primitive in perception-degraded underwater environments
