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pro vyhledávání: '"Enan, Sadman Sakib"'
Diver Identification Using Anthropometric Data Ratios for Underwater Multi-Human-Robot Collaboration
Recent advances in efficient design, perception algorithms, and computing hardware have made it possible to create improved human-robot interaction (HRI) capabilities for autonomous underwater vehicles (AUVs). To conduct secure missions as underwater
Externí odkaz:
http://arxiv.org/abs/2310.00146
Autor:
Enan, Sadman Sakib, Sattar, Junaed
Many underwater tasks, such as cable-and-wreckage inspection, search-and-rescue, benefit from robust human-robot interaction (HRI) capabilities. With the recent advancements in vision-based underwater HRI methods, autonomous underwater vehicles (AUVs
Externí odkaz:
http://arxiv.org/abs/2209.14447
In this paper, we present a motion-based robotic communication framework that enables non-verbal communication among autonomous underwater vehicles (AUVs) and human divers. We design a gestural language for AUV-to-AUV communication which can be easil
Externí odkaz:
http://arxiv.org/abs/2207.05331
This paper presents a deep-learned facial recognition method for underwater robots to identify scuba divers. Specifically, the proposed method is able to recognize divers underwater with faces heavily obscured by scuba masks and breathing apparatus.
Externí odkaz:
http://arxiv.org/abs/2011.09556
Autor:
Islam, Md Jahidul, Edge, Chelsey, Xiao, Yuyang, Luo, Peigen, Mehtaz, Muntaqim, Morse, Christopher, Enan, Sadman Sakib, Sattar, Junaed
In this paper, we present the first large-scale dataset for semantic Segmentation of Underwater IMagery (SUIM). It contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants
Externí odkaz:
http://arxiv.org/abs/2004.01241
Autor:
Edge, Chelsey, Enan, Sadman Sakib, Fulton, Michael, Hong, Jungseok, Mo, Jiawei, Barthelemy, Kimberly, Bashaw, Hunter, Kallevig, Berik, Knutson, Corey, Orpen, Kevin, Sattar, Junaed
In this paper we present LoCO AUV, a Low-Cost, Open Autonomous Underwater Vehicle. LoCO is a general-purpose, single-person-deployable, vision-guided AUV, rated to a depth of 100 meters. We discuss the open and expandable design of this underwater ro
Externí odkaz:
http://arxiv.org/abs/2003.09041
We present a deep residual network-based generative model for single image super-resolution (SISR) of underwater imagery for use by autonomous underwater robots. We also provide an adversarial training pipeline for learning SISR from paired data. In
Externí odkaz:
http://arxiv.org/abs/1909.09437
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