In this article, we propose a tightly coupled visual-inertial-acoustic sensor fusion method to improve the autonomous localization accuracy of underwater vehicles. To address the performance degradation encountered by existing visual or visual-inertial simultaneous localization and mapping systems when applied in underwater environments, we integrate the Doppler velocity log (DVL), an acoustic velocity sensor, to provide additional motion information. To fully leverage the complementary characteristics among visual, inertial, and acoustic sensors, we perform multimodal information fusion in both frontend tracking and backend mapping processes. Specifically, in the frontend tracking process, we first predict the vehicle's pose using the angular velocity measurements from the gyroscope and linear velocity measurements from the DVL. Thereafter, measurements performed by the three sensors between adjacent camera frames are utilized to construct visual reprojection error, inertial error, and DVL displacement error, which are jointly minimized to obtain a more accurate pose estimation at the current frame. In the backend mapping process, we utilize gyroscope and DVL measurements to construct relative pose change residuals between keyframes, which are minimized together with visual and inertial residuals to further refine the poses of the keyframes within the local map. Experimental results on both simulated and real-world underwater datasets demonstrate that the proposed fusion method improves the localization accuracy by more than 30% compared to the current state-of-the-art ORB-SLAM3 stereo-inertial method, validating the potential of the proposed method in practical underwater applications.

The application potential of robotic fish embedded with intelligent visual navigation algorithms in underwater autonomous operation is on full display recently. However, the existing visual navigation methods are limited by the underwater visual conditions and the motion characteristics of robotic fish. To this end, this paper proposes a novel autonomous navigation framework integrated with visual stabilization control. In practice, a stereo vision-based navigation network is proposed to generate the guidance law. On this basis, a biomimetic robotic hammerhead shark with a controllable cephalofoil is developed, and a nonlinear model predictive controller for cephalofoil stabilization relying on the dynamic model is elaborately designed. Extensive simulations and underwater experiments are conducted to validate the effectiveness and superiority of the proposed methods, which significantly enhance exploration efficiency and reduce image jitter by 26.02% compared to the traditional methods. The obtained results provide a new idea for underwater robots to autonomously explore the ocean.Note to Practitioners—This paper is motivated by the problem of vision-based underwater autonomous navigation for a biomimetic robotic fish that possesses underwater visual stability and good maneuverability. The existing visual navigation networks usually generate unexpected navigation instructions when dealing with complex or ambiguous underwater scenes. Additionally, image jitter caused by the rhythmic motion of robotic fish can lead to navigation failure. This paper suggests an integrated navigation framework that includes a biomimetic platform design, a visual stabilization controller, and an intelligent underwater navigation network. Specifically, a novel sphyrnidae-inspired robotic shark is designed as a new platform with superior motion performance. To enhance underwater visual stability, a nonlinear model predictive control-based visual stabilization controller is proposed. Furthermore, a deep stereo attention navigation network based on a parallax attention mechanism is proposed to improve the generalization of vision-based autonomous navigation. A series of underwater search experiments on the robotic shark demonstrate the effectiveness and superiority of the proposed navigation framework. Hopefully, our proposed methods can provide valuable guidance and support for universal underwater robot navigation to accomplish practical marine tasks, such as underwater rescue, resource exploitation, biological observation, and so on.

This paper proposes a tightly-coupled visual-Doppler-Velocity-Log (visual-DVL) fusion method for underwater robot localization through integrating the velocity measurements from a DVL into a visual odometry (VO). Considering that employing the DVL measurements in dead-reckoning systems easily leads to error accumulation and suboptimal results in previous works, we directly integrate them into the visual tracking process. Specifically, the velocity measurements are utilized to improve the initial estimation of camera pose during visual tracking, aiming to provide a better initial value for pose optimization. Thereafter, these velocity measurements are also directly employed to constrain the position change of the camera between two adjacent frames by constructing a novel DVL error term, which is optimized jointly with the visual constrains to obtain a more accurate camera pose. Various experiments are carried out in the datasets collected from several scenarios of the underwater simulation environment HoloOcean, and the results illustrate that the proposed fusion method can effectively improve the localization accuracy for underwater robots by about 20% compared to pure visual odometry. The proposed method provides valuable guidance for the accurate localization of underwater robots.

Robust vision restoration of underwater images remains a challenge. Owing to the lack of well-matched underwater and in-air images, unsupervised methods based on the cyclic generative adversarial framework have been widely investigated in recent years. However, when using an end-to-end unsupervised approach with only unpaired image data, mode collapse could occur, and the color correction of the restored images is usually poor. In this paper, we propose a data- and physics-driven unsupervised architecture to perform underwater image restoration from unpaired underwater and in-air images. For effective color correction and quality enhancement, an underwater image degeneration model must be explicitly constructed based on the optically unambiguous physics law. Thus, we employ the Jaffe-McGlamery degeneration theory to design a generator and use neural networks to model the process of underwater visual degeneration. Furthermore, we impose physical constraints on the scene depth and degeneration factors for backscattering estimation to avoid the vanishing gradient problem during the training of the hybrid physical-neural model. Experimental results show that the proposed method can be used to perform high-quality restoration of unconstrained underwater images without supervision. On multiple benchmarks, the proposed method outperforms several state-of-the-art supervised and unsupervised approaches. We demonstrate that our method yields encouraging results in real-world applications.

This paper proposes a multi-objective visionbased navigation network for biomimetic underwater robots to cope with scientific observation, target selection, and obstacle avoidance in marine missions. Structurally, a stereo block attention module is first constructed to serially extract the channel and spatial attention portion of the real-time visual feedback. Next, the parallax attention mechanism is introduced to enable the network to excavate implicit parallax information in stereo pairs, effectively eliminating the oscillation of the network output in the presence of ambiguous visual input. Further, with the assistance of other low-cost sensors, the proposed navigation network can be expanded in some largescale application scenarios, such as sparse coral observation. Finally, underwater simulations reveal that the proposed method obtains significantly improved control effect and real-time ability, compared with other related works. In particular, based on a self-developed biomimetic robotic dolphin, collision-free simulations with a cumulative distance beyond 1000 m were carried out and validated the effectiveness and the superiority of the navigation network, where both dense and sparse targets were fully tested. The robotic dolphin can not only successfully conduct accurate coral observation without collision, but also quest the observation targets as much as possible in the area where the observation targets are concentrated. The proposed network provides an intelligent and efficient navigation scheme for autonomous underwater operation of small-size underwater robots.