Authors - Neha Kriti, Arti Devi, Sarthak Srivastava, Varun Dutt Abstract - Localization in Autonomous Underwater Vehicles (AUVs) continues to be a major challenge in GPS-denied environments, where inertial drift, low visibility and uncertain motion models frequently un dermine state estimation. In this paper, we present our visual-inertial odometry framework A-KIT VIO specifically designed for resilient pose tracking underwater. The system employs tightly coupled monocular camera observations with IMU data using an Extended Kalman Filter to maintain high-rate inertial propagation along with feature-based vi sual updates to avoid drift. To address the frequent covariance mismatch during non-stationary maneuvers, we added a transformer-based module to adaptively adjust IMU process noise based on the vehicle’s immediate motion context. This method of uncertainty modeling ensures filter sta bility in scenarios where standard, fixed-noise configurations typically diverge. Evaluated within a Gazebo-based underwater simulation, the framework demonstrated consistent state estimation and bounded drift over long-range trajectories, highlighting the efficacy of adaptive covari ance for reliable underwater localization.
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