AQUA-SLAM：带传感器标定的紧耦合水下声学-视觉-惯性SLAM
AQUA-SLAM: Tightly-Coupled Underwater Acoustic-Visual-Inertial SLAM with Sensor Calibration
- Imperial College London（帝国理工学院）
- Heriot-Watt University（赫瑞瓦特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对水下视觉SLAM受能见度与特征丢失影响的问题，提出紧耦合AQUA-SLAM融合DVL、立体相机和IMU，并实现在线多传感器外参与DVL对准标定，水池与北海实验证明其精度和鲁棒性优于现有方法。
AI中文摘要：
水下环境因能见度有限、照明不足以及图像中结构特征偶发丢失，给视觉同步定位与建图（SLAM）系统带来了严峻挑战。为应对这些挑战，本文提出一种新颖的紧耦合声学-视觉-惯性SLAM方法，名为AQUA-SLAM，在图优化框架内融合多普勒计程仪（DVL）、立体相机和惯性测量单元（IMU）。此外，我们提出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换能器未对准标定，并采用快速线性近似程序实现实时在线执行。所提方法在具备真值的水池环境中进行了广泛评估，并在北海的近海应用中得到验证。结果表明，在定位精度和鲁棒性方面，我们的方法优于当前最先进的水下及视觉-惯性SLAM系统。所提系统将向社区开源。
英文摘要：
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.
