Underwater environments pose significant challenges for visual Simultaneous Localization and Mapping (SLAM) systems due to limited visibility, inadequate illumination, and sporadic loss of structural features in images. Addressing these challenges, this paper introduces a novel, tightly-coupled Acoustic-Visual-Inertial SLAM approach, termed AQUA-SLAM, to fuse a Doppler Velocity Log (DVL), a stereo camera, and an Inertial Measurement Unit (IMU) within a graph optimization framework. Moreover, we propose an efficient sensor calibration technique, encompassing multi-sensor extrinsic calibration (among the DVL, camera and IMU) and DVL transducer misalignment calibration, with a fast linear approximation procedure for real-time online execution. The proposed methods are extensively evaluated in a tank environment with ground truth, and validated for offshore applications in the North Sea. The results demonstrate that our method surpasses current state-of-the-art underwater and visual-inertial SLAM systems in terms of localization accuracy and robustness. The proposed system will be made open-source for the community.

翻译：水下环境因能见度有限、光照不足以及图像中结构特征间歇性缺失，对视觉同时定位与地图构建（SLAM）系统提出了重大挑战。为应对这些挑战，本文提出了一种新颖的、紧密耦合的声学-视觉-惯性SLAM方法，命名为AQUA-SLAM，该方法在图优化框架内融合了多普勒速度计程仪（DVL）、立体相机和惯性测量单元（IMU）。此外，我们提出了一种高效的传感器标定技术，涵盖多传感器外参标定（DVL、相机与IMU之间）以及DVL换能器安装偏差标定，并采用一种快速线性近似方法以实现实时在线执行。所提出的方法在有真值数据的实验水池环境中进行了全面评估，并在北海的离岸应用中得到了验证。结果表明，在定位精度和鲁棒性方面，我们的方法超越了当前最先进的水下及视觉-惯性SLAM系统。所提出的系统将开源供社区使用。