融合视觉、惯性与声呐，实现水下高精度实时三维重建
VISO: Robust Underwater Visual-Inertial-Sonar SLAM with Photometric Rendering for Dense 3D Reconstruction
- 三传感器融合：相机+惯导+3D声呐，提升水下定位鲁棒性
- 提出粗到细在线标定法，精准估计声呐与相机外参
- 声呐点云光度渲染，使重建结果更接近真实视觉效果
水下视觉环境复杂，严重影响基于视觉的定位精度与高保真密集三维重建。本文提出VISO，一种融合双目相机、惯性测量单元（IMU）和3D声呐的鲁棒水下SLAM系统，实现精确6自由度定位，并支持高效且具有高光度保真的密集3D重建。提出一种从粗到精的在线外参标定方法，用于估计3D声呐与相机间的外部参数。此外，设计了一种光度渲染策略，将视觉信息融入3D声呐点云，丰富声呐地图的视觉特征。在实验室水箱和开放湖泊中的大量实验表明，VISO在定位鲁棒性和准确性方面优于当前最先进的水下及视觉SLAM算法，同时实现了与离线密集映射方法相当的实时密集三维重建性能。
原文摘要 · Abstract (English)
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.
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