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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment -  Xiali Wang,  Xiaochun Wang,  Don Mitchell Wilkes

Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment (eBook)

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2019 | 1st ed. 2020
XXII, 328 Seiten
Springer Singapore (Verlag)
978-981-13-9217-7 (ISBN)
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This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.

Xiaochun Wang received her BS degree from Beijing University and the PhD degree from the Department of Electrical Engineering and Computer Science, Vanderbilt University. She is currently an associate professor of School of Software Engineering at Xi'an Jiaotong University. Her research interests are in computer vision, signal processing, and pattern recognition.

Xia Li Wang received the PhD degree from the Department of Computer Science, Northwest University, China, in 2005. He is a faculty member in the Department of Computer Science, Changan University, China. His research interests are in computer vision, signal processing, intelligent traffic system, and pattern recognition.

D. Mitchell Wilkes received the BSEE degree from Florida Atlantic, and the MSEE and PhD degrees from Georgia Institute of Technology. His research interests include digital signal processing, image processing and computer vision, structurally adaptive systems, sonar, as well as signal modeling. He is a member of the IEEE and a faculty member at the Department of Electrical Engineering and Computer Science, Vanderbilt University. He is a member of the IEEE.

This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.
Erscheint lt. Verlag 12.8.2019
Zusatzinfo XXII, 328 p. 99 illus., 78 illus. in color.
Sprache englisch
Themenwelt Informatik Grafik / Design Digitale Bildverarbeitung
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Technik Elektrotechnik / Energietechnik
Technik Maschinenbau
Schlagworte Autonomous Mobile Robot Localization • Computer Vision and Machine Perception • Hippocampal Configural Represenations • machine learning methodologies • Natural Scene Recognition in An Unknown Environment • Prefrontal Cortex Working Memory System
ISBN-10 981-13-9217-X / 981139217X
ISBN-13 978-981-13-9217-7 / 9789811392177
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