Machine Vision for Improved Human-Robot Cooperation in Adverse Underwater Conditions
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by
Md Jahidul Islam
2021
Abstract
Visually-guided underwater robots are deployed alongside human divers for
cooperative exploration, inspection, and monitoring tasks in numerous
shallow-water and coastal-water applications. The most essential capability of
such companion robots is to visually interpret their surroundings and assist
the divers during various stages of an underwater mission. Despite recent
technological advancements, the existing systems and solutions for real-time
visual perception are greatly affected by marine artifacts such as poor
visibility, lighting variation, and the scarcity of salient features. The
difficulties are exacerbated by a host of non-linear image distortions caused
by the vulnerabilities of underwater light propagation (e.g.,
wavelength-dependent attenuation, absorption, and scattering). In this
dissertation, we present a set of novel and improved visual perception
solutions to address these challenges for effective underwater human-robot
cooperation.
Specifically, we develop robust and efficient modules for Autonomous
Underwater Vehicles (AUVs) to follow and interact with companion divers by
accurately perceiving their surroundings while relying on noisy visual sensing
alone. Moreover, our proposed perception solutions enable visually-guided
robots to see better in noisy sensing conditions and do better with limited
computational resources and real-time constraints. The research outcomes entail
novel design and efficient implementation of the underlying vision and
learning-based algorithms with extensive field experimental validations and
feasibility analyses for single-board deployments. In addition to advancing the
state-of-the-art, the proposed methodologies and systems take us one step
closer toward bridging the gap between theory and practice for improved
human-robot cooperation in the wild.
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