Self-Supervised Monocular Depth Underwater

Depth estimation is critical for any robotic system. In the past years, the estimation of depth from monocular images has shown great improvement. However, in the underwater environment results are still lagging behind due to appearance changes caused by the medium. So far little effort has been invested in overcoming this. Moreover, underwater, there are more limitations to using high-resolution depth sensors, which is a serious obstacle to generating ground truth. So far unsupervised methods that tried to solve this have achieved limited success as they relied on domain transfer from a dataset in the air. We suggest network training using subsequent frames, self-supervised by a reprojection loss, as was demonstrated successfully above water. We propose several additions to the self-supervised framework to cope with the underwater environment and achieve state-of-the-art results on a challenging forward-looking underwater dataset.

I. Introduction

There is a wide range of target applications for depth estimation, from obstacle detection to object measurement and from 3D reconstruction to image enhancement. Underwater depth estimation (note that here depth refers to the object range, and not to the depth underwater) is important for Autonomous Underwater Vehicles (AUVs) [15] (Fig. 1), localization and mapping, motion planning, and image dehazing [6]. As such inferring depth from vision systems has been widely investigated in the last years. There is a range of sensors and imaging setups that can provide depth, such as stereo, multiple-view, and time-of-flight (ToF) [11], [12], [23]. Monocular depth estimation is different from other vision systems in the sense that it uses a single RGB image with no special setup or hardware, and as such has many advantages. Because of mechanical design considerations, in many AUVs, it is difficult to place a stereo setup with a baseline that is wide enough, so monocular depth there is particularly attractive and can be combined with other sensors (e.g., Sonars) to set the scale.