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• W3183141888 abstract "At the heart of developing human-robotic systems is the usage of passive 2D image sensors to extrapolate viable 3D (localization) information for higher-order tasks. However, under ambiguous environments, notions of mistrust begins to form between an overseeing physical operator and the deployed autonomous system that generates blackbox actions. Ideally, there should be a natural interplay between the operator and autonomy whereby decision making is ultimately driven by an operator “on-the-fly” yet also informed by the autonomous counterpart that may assist in such decisions. To do so, both physical operators and their autonomous actions should be canonically equivalent. That is, pure human-vision navigation generally “segments” (visualizes) 2D information for 3D localization in a joint and coupled fashion. On the other hand, robotic vision task of localization should also be naturally coupled yet is often decoupled (i.e., 2D image segmentation and 2D3D pose estimation are often considered different tasks, but are in fact chicken-and-egg problems in regards to 3D localization). To this end, the main aim of this note is to introduce a 2D3D feedback control theoretic framework to fuse such concepts in order to inject real-time operator in-loop to provide control (if needed) in ambiguous situations. The resulting framework can then be naturally extendable to real-time robotic systems." @default.
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• W3183141888 date "2021-05-25" @default.
• W3183141888 modified "2023-09-25" @default.
• W3183141888 title "Forming 2D3D Operator Control for Ambiguous Image Environments" @default.
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• W3183141888 doi "https://doi.org/10.23919/acc50511.2021.9482826" @default.
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