Abstract
In the context of robotic underwater operations, the visual degradations induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, most localization methods are based on expensive navigational sensors associated with acoustic positioning. On the other hand, visual odometry and visual SLAM have been exhaustively studied for aerial or terrestrial applications, but state-of-the-art algorithms fail underwater. In this paper we tackle the problem of using a simple low-cost camera for underwater localization and propose a new monocular visual odometry method dedicated to the underwater environment. We evaluate different tracking methods and show that optical flow based tracking is more suited to underwater images than classical approaches based on descriptors. We also propose a keyframe-based visual odometry approach highly relying on nonlinear optimization. The proposed algorithm has been assessed on both simulated and real underwater datasets and outperforms state-of-the-art visual SLAM methods under many of the most challenging conditions. The main application of this work is the localization of Remotely Operated Vehicles (ROVs) used for underwater archaeological missions but the developed system can be used in any other applications as long as visual information is available.
Abstract (translated)
在机器人水下操作的背景下，由介质特性引起的视觉退化使得摄像机专用于本地化目的变得困难。因此，大多数定位方法基于与声学定位相关联的昂贵的导航传感器。另一方面，视觉测距和视觉SLAM已经针对航空或地面应用进行了详尽的研究，但最先进的算法在水下失败。在本文中，我们解决了使用简单的低成本相机进行水下定位的问题，并提出了一种专用于水下环境的新型单目视觉测距方法。我们评估不同的跟踪方法，并表明基于光学流的跟踪比基于描述符的经典方法更适合于水下图像。我们还提出了一种基于关键帧的视觉测距方法，高度依赖于非线性优化。所提出的算法已经在模拟和真实水下数据集上进行了评估，并且在许多最具挑战性的条件下优于最先进的视觉SLAM方法。这项工作的主要应用是用于水下考古任务的远程操作车辆（ROV）的本地化，但只要有可用的视觉信息，开发的系统就可以用于任何其他应用。
URL
https://arxiv.org/abs/1806.05842
