Visual-Inertial-Acoustic Sensor Fusion for Accurate Autonomous Localization of Underwater Vehicles
提出一种紧耦合的视觉-惯性-声学传感器融合方法，通过集成多普勒速度计程仪提升水下航行器定位精度，在模拟和真实数据集上相比ORB-SLAM3方法精度提高超过30%。
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.