融合视觉与声呐数据，实现在浑浊水下环境的实时场景重建。
Opti-Acoustic Scene Reconstruction in Highly Turbid Underwater Environments
- 不依赖特征点，通过区域匹配融合图像与声呐信息。
- 在不同浑浊度下重建精度优于纯视觉与纯声呐方法。
- 适合水下机器人在复杂水域导航，代码已开源。
水下机器人在靠近结构物时进行场景重建至关重要。单目视觉方法在浑浊水中不可靠且缺乏深度尺度信息，而声呐虽能适应浑浊水和非均匀光照，但分辨率低且存在高程歧义。本文提出一种针对浑浊水环境优化的实时光声场景重建方法。该策略避免在视觉数据中识别特征点，转而识别数据中的兴趣区域，并将图像中的相关区域与对应声呐数据匹配。通过利用声呐提供的距离数据和相机图像提供的高程数据，实现场景重建。在不同浑浊度下的实验对比，以及在码头环境中的实地测试，验证了该方法的有效性。我们已开源代码，以促进复现与社区参与。
原文摘要 · Abstract (English)
Scene reconstruction is an essential capability for underwater robots navigating in close proximity to structures. Monocular vision-based reconstruction methods are unreliable in turbid waters and lack depth scale information. Sonars are robust to turbid water and non-uniform lighting conditions, however, they have low resolution and elevation ambiguity. This work proposes a real-time opti-acoustic scene reconstruction method that is specially optimized to work in turbid water. Our strategy avoids having to identify point features in visual data and instead identifies regions of interest in the data. We then match relevant regions in the image to corresponding sonar data. A reconstruction is obtained by leveraging range data from the sonar and elevation data from the camera image. Experimental comparisons against other vision-based and sonar-based approaches at varying turbidity levels, and field tests conducted in marina environments, validate the effectiveness of the proposed approach. We have made our code open-source to facilitate reproducibility and encourage community engagement.
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