Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters
Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters
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DOI：
10.1145/3366486.3366523
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发表时间：
2019-10
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影响因子：
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通讯作者：
Wenjie Chen;M. Rahmati;Vidyasagar Sadhu;D. Pompili
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文献类型：
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作者：
Wenjie Chen;M. Rahmati;Vidyasagar Sadhu;D. Pompili
经典的基于视觉的导航解决方案用于同步定位和地图绘制（SLAM）等算法，当水浑浊且记录图像质量较低时，通常无法在水下工作。这是因为大多数SLAM算法是基于特征的技术，并且通常不可能从模糊的水下图像中提取匹配特征。为了获得更多有用的特征，可以在将图像用于导航/定位算法之前使用图像处理技术对图像进行去雾。图像复原的方法有很多，但各种方法的增强程度和资源消耗各不相同。在本文中，我们提出了一种新的视觉SLAM，专门为水下环境设计的，使用生成对抗网络（GANs），以提高水下图像的质量与水下图像质量评价指标。该方法提高了SLAM的效率，并获得了较好的导航和定位精度。我们通过使用水中不同浊度水平的不同图像来评估所提出的GANs-SLAM组合。进行了实验，数据取自美国普林斯顿的卡内基湖和新泽西的拉里坦河。
Classic vision-based navigation solutions, which are utilized in algorithms such as Simultaneous Localization and Mapping (SLAM), usually fail to work underwater when the water is murky and the quality of the recorded images is low. That is because most SLAM algorithms are feature-based techniques and often it is impossible to extract the matched features from blurry underwater images. To get more useful features, image processing techniques can be used to dehaze the images before they are used in a navigation/localization algorithm. There are many well-developed methods for image restoration, but the degree of enhancement and the resource cost of the methods are different. In this paper, we propose a new visual SLAM, specifically-designed for the underwater environment, using Generative Adversarial Networks (GANs) to enhance the quality of underwater images with underwater image quality evaluation metrics. This procedure increases the efficiency of SLAM and gets a better navigation and localization accuracy. We evaluate the proposed GANs-SLAM combination by using different images with various levels of turbidity in the water. Experiments were conducted and the data was extracted from the Carnegie Lake in Princeton, and the Raritan river both in New Jersey, USA.