Abstract
In the context of underwater robotics, the visual degradation induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, many underwater localization methods are based on expensive navigation sensors associated with acoustic positioning. On the other hand, pure visual localization methods have shown great potential in underwater localization but the challenging conditions, such as the presence of turbidity and dynamism, remain complex to tackle. In this paper, we propose a new visual odometry method designed to be robust to these visual perturbations. The proposed algorithm has been assessed on both simulated and real underwater datasets and outperforms state-of-the-art terrestrial visual SLAM methods under many of the most challenging conditions. The main application of this work is the localization of Remotely Operated Vehicles used for underwater archaeological missions, but the developed system can be used in any other applications as long as visual information is available.
Highlights
Accurate localization is critical for most robotic underwater operations, especially when navigating in areas with obstacles such as rocks, shipwrecks or Oil & Gas structures
In this paper we have presented UW-Visual Odometry (VO), a new vision-based underwater localization method
While most of the existing approaches rely on expensive navigational sensors to estimate the motions of underwater vehicles, we have chosen to investigate the use of a simple monocular camera as a mean of localization
Summary
Accurate localization is critical for most robotic underwater operations, especially when navigating in areas with obstacles such as rocks, shipwrecks or Oil & Gas structures. Localization from vision is more complex than in aerial and terrestrial environments and state-of-the-art open-source VO or VSLAM algorithms fail when the operating conditions become too harsh [9,10] This is mainly due to the visual degradation caused by the medium specific properties. In the darkness of deep waters, the fauna is a cause of visual degradation as animals are attracted by the artificial light and tend to get in the field of view of the camera, leading to dynamism and occlusions in the images In front of these difficulties, many works tackle the underwater localization problem using sonar systems [11,12,13], as they do not suffer from these visual degradation. We show that UW-VO competes with these methods in terms of accuracy and surpasses them in terms of robustness
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