Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments

TL;DR

This paper introduces a novel monocular visual odometry method tailored for underwater environments, enabling low-cost localization of ROVs despite visual degradation, outperforming existing SLAM techniques in challenging conditions.

Contribution

The paper presents a new underwater-specific monocular visual odometry algorithm using optical flow and nonlinear optimization, optimized for turbid and dynamic underwater scenes.

Findings

Optical flow tracking outperforms descriptor-based methods underwater.

The proposed method surpasses state-of-the-art visual SLAM in challenging conditions.

Validated on simulated and real underwater datasets.

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.