Contact-Aided Factor-Graph Localization for Underwater Sampling

2026-08-27

AI Summary

为解决水下采样时自主水下航行器状态估计不准的问题，提出了一种基于接触辅助因子图的定位框架，融合了吸盘接触事件、自适应视觉里程计及传感器信息。

Abstract

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

Problem

• state estimation
• autonomous underwater vehicles
• scale ambiguity
• lateral degeneracy
• inertial-Doppler Velocity Log (DVL)

Innovation

• Contact-Aided Factor-Graph Localization
• Physical Interaction as Geometric Constraint
• Adaptive Visual Odometry
• Implicit Loop Closures
• Underwater Sampling