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.

本文提出了一种适用于水下环境的单目视觉里程计方法，该方法基于光流跟踪和基于关键帧的非线性优化，并在模拟和实际水下数据集上验证其性能优于现有的视觉 SLAM 方法，主要应用于水下考古任务中 ROV 的定位。