  In this paper, we present a fast monocular depth estimation method for
enabling 3D perception capabilities of low-cost underwater robots. We formulate
a novel end-to-end deep visual learning pipeline named UDepth, which
incorporates domain knowledge of image formation characteristics of natural
underwater scenes. First, we adapt a new input space from raw RGB image space
by exploiting underwater light attenuation prior, and then devise a
least-squared formulation for coarse pixel-wise depth prediction. Subsequently,
we extend this into a domain projection loss that guides the end-to-end
learning of UDepth on over 9K RGB-D training samples. UDepth is designed with a
computationally light MobileNetV2 backbone and a Transformer-based optimizer
for ensuring fast inference rates on embedded systems. By domain-aware design
choices and through comprehensive experimental analyses, we demonstrate that it
is possible to achieve state-of-the-art depth estimation performance while
ensuring a small computational footprint. Specifically, with 70%-80% less
network parameters than existing benchmarks, UDepth achieves comparable and
often better depth estimation performance. While the full model offers over 66
FPS (13 FPS) inference rates on a single GPU (CPU core), our domain projection
for coarse depth prediction runs at 51.5 FPS rates on single-board NVIDIA
Jetson TX2s. The inference pipelines are available at
https://github.com/uf-robopi/UDepth.
