Deep Learning for Visual Navigation of Underwater Robots
M. Sunbeam
This paper aims to briefly survey deep learning methods for visual navigation
of underwater robotics. The scope of this paper includes the visual perception
of underwater robotics with deep learning methods, the available visual
underwater datasets, imitation learning, and reinforcement learning methods for
navigation. Additionally, relevant works will be categorized under the
imitation learning or deep learning paradigm for underwater robots for clarity
of the training methodologies in the current landscape. Literature that uses
deep learning algorithms to process non-visual data for underwater navigation
will not be considered, except as contrasting examples.