  In this paper, we present a number of robust methodologies for an underwater
robot to visually detect, follow, and interact with a diver for collaborative
task execution. We design and develop two autonomous diver-following
algorithms, the first of which utilizes both spatial- and frequency-domain
features pertaining to human swimming patterns in order to visually track a
diver. The second algorithm uses a convolutional neural network-based model for
robust tracking-by-detection. In addition, we propose a hand gesture-based
human-robot communication framework that is syntactically simpler and
computationally more efficient than the existing grammar-based frameworks. In
the proposed interaction framework, deep visual detectors are used to provide
accurate hand gesture recognition; subsequently, a finite-state machine
performs robust and efficient gesture-to-instruction mapping. The
distinguishing feature of this framework is that it can be easily adopted by
divers for communicating with underwater robots without using artificial
markers or requiring memorization of complex language rules. Furthermore, we
validate the performance and effectiveness of the proposed methodologies
through extensive field experiments in closed- and open-water environments.
Finally, we perform a user interaction study to demonstrate the usability
benefits of our proposed interaction framework compared to existing methods.
