Deep Underwater Monocular Depth Estimation with Single-Beam Echosounder

Abstract

Underwater depth estimation is essential for safe Autonomous Underwater Vehicles (AUV) navigation. While there has been recent advances in out-of-water monocular depth estimation, it is difficult to apply these methods to the underwater domain due to the lack of well-established datasets with labelled ground truths. In this paper, we propose a novel method for self-supervised underwater monocular depth estimation by leveraging a low-cost single-beam echosounder (SBES). We also present a synthetic dataset for underwater depth estimation to facilitate visual learning research in the underwater domain, available at https://github.com/hdacnw/sbes-depth. We evaluated our method on the proposed dataset with results outperforming previous methods and tested our method in a dataset we collected with an inexpensive AUV. We further investigated the use of SBES as an additional component in our self-supervised method for up-to-scale depth estimation providing insights on next research directions.