融合声学、视觉与惯性数据，提升水下定位精度与鲁棒性。
AQUA-SLAM: Tightly-Coupled Underwater Acoustic-Visual-Inertial SLAM with Sensor Calibration
- 紧耦合融合声呐、相机与惯性传感器，优化定位结果。
- 在实验池与北海海域验证，定位精度优于现有系统。
- 支持实时校准，适合水下机器人与海洋探测应用。
水下环境因能见度低、光照不足及图像中特征缺失，给视觉同步定位与地图构建（SLAM）带来挑战。本文提出一种新型紧耦合声学-视觉-惯性SLAM方法AQUA-SLAM，将多普勒速度计（DVL）、双目相机与惯性测量单元（IMU）融合于图优化框架中。同时，设计了一种高效传感器标定技术，涵盖多传感器外部参数标定（DVL、相机与IMU间）及声呐换能器偏移校准，并采用快速线性逼近法实现实时在线标定。所提方法在带真值的水池环境中进行了广泛评估，并在北海近海场景中完成实际验证。结果表明，本方法在定位精度与鲁棒性方面均优于当前最先进的水下及视觉惯性SLAM系统。相关系统将开源发布。
原文摘要 · Abstract (English)
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.
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