We present a robust underwater navigation system that integrates magnetic, visual, and inertial measurements from commercial off-the-shelf sensors. Visual Inertial Navigation Systems (VINS) face challenges when used for Autonomous Underwater Vehicle (AUV) localization in perceptually degraded environments. First, traditional VINS methods struggle to accurately detect sufficient loops due to several factors: feature scarcity, environmental similarities, limited visibility, orientation changes, and constrained computational resources. Second, the yaw is unobservable in VINS and it may drift rapidly without distinct features. To address these issues, we propose a novel system that enhances loop closure by fusing magnetic signatures from a low-cost alternating magnetic field coil with multi-scale mapping and hierarchical place recognition. Additionally, we utilize geomagnetic fields to align feature descriptors, improving robustness to orientation variations. Our system also refines yaw estimations by leveraging geomagnetic data, aligning them with global references to mitigate drift. Experimental results validate the improved performance of the proposed system.

Sonar images have the characteristics of lower resolution and blurrier edges compared to optical images, which make the feature-matching method in underwater target tracking less robust. To solve this problem, we propose a particle filter (PF)-based underwater target-tracking method utilizing Zernike moment feature matching. Zernike moments are used to construct the feature-description vector for feature matching and contribute to the update of particle weights. In addition, the particle state transition method is optimized by using a first-order autoregressive model. In this paper, we compare Hu moments and Zernike moments, and we also compare whether to optimize the particle state transition on the tracking results or not based on the effects of each option. The experimental results based on the AUV (autonomous underwater vehicle) prove that the robustness and accuracy of this innovative method is better than the other combined methods mentioned in this paper.

We present a sensor misalignment-tolerant AUV navigation method that leverages measurements from an acoustic array and Dead Reckoning (DR). Recent studies have demonstrated the potential use of passive acoustic Direction of Arrival (DoA) measurements for AUV navigation without requiring ranging measurements. However, position estimation accuracy degrades at shallow elevation angles, and the issue of sensor misalignment between the acoustic array and the attitude sensor has not been comprehensively investigated. To address this, we propose the incorporation of Doppler speed measurements, which significantly enhance navigation accuracy, as demonstrated through theoretical analysis and experiments. Since sensor misalignment deteriorates navigation accuracy, we also present a novel approach for simultaneous AUV navigation, beacon localization, and real-time sensor self-alignment, eliminating the need for external high-precision sensors. An Unscented Kalman Filter (UKF) that enables the necessary calculations to be completed at an affordable computational load is developed. A Nonlinear Least Squares (NLS)-based technique is employed to find an initial solution for beacon localization and sensor alignment as early as possible using a short-term window of measurements. Experimental results validate the effectiveness of our proposed method, demonstrating substantial improvements in performance.

We present a sensor misalignment-tolerant AUV navigation method that leverages measurements from an acoustic array and dead reckoned information. Recent studies have demonstrated the potential use of passive acoustic Direction of Arrival (DoA) measurements for AUV navigation without requiring ranging measurements. However, the sensor misalignment between the acoustic array and the attitude sensor was not accounted for. Such misalignment may deteriorate the navigation accuracy. This paper proposes a novel approach that allows simultaneous AUV navigation, beacon localization, and sensor alignment. An Unscented Kalman Filter (UKF) that enables the necessary calculations to be completed at an affordable computational load is developed. A Nonlinear Least Squares (NLS)-based technique is employed to find an initial solution for beacon localization and sensor alignment as early as possible using a short-term window of measurements. Experimental results demonstrate the performance of the proposed method.