Visual challenges in underwater environments significantly hinder the accuracy of vision-based localisation and the high-fidelity dense reconstruction. In this paper, we propose VISO, a robust underwater SLAM system that fuses a stereo camera, an inertial measurement unit (IMU), and a 3D sonar to achieve accurate 6-DoF localisation and enable efficient dense 3D reconstruction with high photometric fidelity. We introduce a coarse-to-fine online calibration approach for extrinsic parameters estimation between the 3D sonar and the camera. Additionally, a photometric rendering strategy is proposed for the 3D sonar point cloud to enrich the sonar map with visual information. Extensive experiments in a laboratory tank and an open lake demonstrate that VISO surpasses current state-of-the-art underwater and visual-based SLAM algorithms in terms of localisation robustness and accuracy, while also exhibiting real-time dense 3D reconstruction performance comparable to the offline dense mapping method.

翻译：水下环境中的视觉挑战严重制约了基于视觉的定位精度与高保真稠密重建。本文提出VISO，一种鲁棒的水下SLAM系统，通过融合立体相机、惯性测量单元（IMU）和三维声呐，实现精确的六自由度定位，并支持具有高光度保真度的高效稠密三维重建。我们提出了一种从粗到精的在线标定方法，用于估计三维声呐与相机之间的外参。此外，针对三维声呐点云提出了光度渲染策略，通过视觉信息增强声呐地图的丰富性。在实验室水槽和开放湖泊中进行的大量实验表明，VISO在定位鲁棒性与精度方面超越了当前最先进的水下及视觉SLAM算法，同时展现出与离线稠密建图方法相媲美的实时稠密三维重建性能。