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Neuromorphic stereo vision: A survey of bio-inspired sensors and algorithms

Steffen, L.; Reichard, D.; Weinland, J.; Kaiser, J.; Roennau, A.; Dillmann, Rüdiger

Abstract (englisch):
Any visual sensor, whether artificial or biological, maps the 3D-world on a 2D-representation. The missing dimension is depth and most species use stereo vision to recover it. Stereo vision implies multiple perspectives and matching, hence it obtains depth from a pair of images. Algorithms for stereo vision are also used prosperously in robotics. Although, biological systems seem to compute disparities effortless, artificial methods suffer from high energy demands and latency. The crucial part is the correspondence problem; finding the matching points of two images. The development of event-based cameras, inspired by the retina, enables the exploitation of an additional physical constraint—time. Due to their asynchronous course of operation, considering the precise occurrence of spikes, Spiking Neural Networks take advantage of this constraint. In this work, we investigate sensors and algorithms for event-based stereo vision leading to more biologically plausible robots. Hereby, we focus mainly on binocular stereo vision.

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Verlagsausgabe §
DOI: 10.5445/IR/1000096673
Veröffentlicht am 23.07.2019
Originalveröffentlichung
DOI: 10.3389/fnbot.2019.00028
Scopus
Zitationen: 9
Web of Science
Zitationen: 7
Dimensions
Zitationen: 10
Cover der Publikation
Zugehörige Institution(en) am KIT Forschungszentrum Informatik, Karlsruhe (FZI)
Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2019
Sprache Englisch
Identifikator ISSN: 1662-5218
KITopen-ID: 1000096673
Erschienen in Frontiers in neurorobotics
Verlag Frontiers Media
Band 13
Seiten Article No.28
Vorab online veröffentlicht am 28.05.2019
Nachgewiesen in Dimensions
Web of Science
Scopus
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