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Autore: | Wang Xiaochun |
Titolo: | Machine learning-based natural scene recognition for mobile robot localization in an unknown environment / / Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes |
Pubblicazione: | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
Edizione: | 1st edition 2020. |
Descrizione fisica: | 1 online resource (xxii, 328 pages) : illustrations (some color) |
Disciplina: | 006.3 |
Soggetto topico: | Machine learning |
Mobile robots | |
Persona (resp. second.): | WangXiali |
WilkesDon Mitchell | |
Nota di contenuto: | Part I Introduction -- Part II Unsupervised Learning -- Part III Supervised Learning and Semi-Supervised Learning -- Part IV Reinforcement Learning. |
Sommario/riassunto: | 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. |
Titolo autorizzato: | Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment |
ISBN: | 981-13-9217-X |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910484164603321 |
Lo trovi qui: | Univ. Federico II |
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