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

Authors: Wang, Xiaochun, Wang, Xiali, Wilkes, Don Mitchell

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  • Focuses on building bridges between natural and artificial computation
  • Presents an object identification model and two different strategies for online learning
  • Evaluates using a realistic scenario and delivers convincing results
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eBook 83,29 €
price for Spain (gross)
  • ISBN 978-981-13-9217-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 145,59 €
price for Spain (gross)
  • ISBN 978-981-13-9216-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

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.

About the authors

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.

Table of contents (16 chapters)

Table of contents (16 chapters)

Buy this book

eBook 83,29 €
price for Spain (gross)
  • ISBN 978-981-13-9217-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 145,59 €
price for Spain (gross)
  • ISBN 978-981-13-9216-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
Authors
Copyright
2020
Publisher
Springer Singapore
Copyright Holder
Xi'an Jiaotong University Press
Distribution Rights
Distribution rights for print book in mainland China: Xi'an Jiaotong University Press
eBook ISBN
978-981-13-9217-7
DOI
10.1007/978-981-13-9217-7
Hardcover ISBN
978-981-13-9216-0
Edition Number
1
Number of Pages
XXII, 328
Number of Illustrations
21 b/w illustrations, 78 illustrations in colour
Topics