在高浊度水下环境中优化的光学-声学场景重建
Opti-Acoustic Scene Reconstruction in Highly Turbid Underwater Environments
AI总结：
本文提出一种实时光学-声学场景重建方法，通过区域兴趣点匹配和融合声呐与相机数据，有效提升高浊度水下环境中的场景重建性能。
摘要：
场景重建是水下机器人接近结构时的关键能力。基于单目视觉的重建方法在浊水环境中不可靠且缺乏深度尺度信息。声呐在浊水和非均匀光照条件下鲁棒性高，但分辨率低且存在仰角歧义。本文提出一种专门优化用于浊水环境的实时光学-声学场景重建方法。我们的策略避免了在视觉数据中识别点特征，而是识别数据中的感兴趣区域。然后将图像中的相关区域与相应的声呐数据匹配。通过利用声呐的范围数据和相机图像的仰角数据获得重建。在不同浊度水平下的实验比较以及在船坞环境中的实地测试验证了所提方法的有效性。我们已开源代码以促进可重复性和鼓励社区参与。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
