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Classic vision-based navigation solutions, which are utilized in algorithmssuch as Simultaneous Localization and Mapping (SLAM), usually fail to workunderwater when the water is murky and the quality of the recorded images islow. That is because most SLAM algorithms are feature-based techniques andoften it is impossible to extract the matched features from blurry underwaterimages. To get more useful features, image processing techniques can be used todehaze the images before they are used in a navigation/localization algorithm.There are many well-developed methods for image restoration, but the degree ofenhancement and the resource cost of the methods are different. In this paper,we propose a new visual SLAM, specifically-designed for the underwaterenvironment, using Generative Adversarial Networks (GANs) to enhance thequality of underwater images with underwater image quality evaluation metrics.This procedure increases the efficiency of SLAM and gets a better navigationand localization accuracy. We evaluate the proposed GANs-SLAM combination byusing different images with various levels of turbidity in the water.Experiments were conducted and the data was extracted from the Carnegie Lake inPrinceton, and the Raritan river both in New Jersey, USA.

Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters