|
|
|
Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
|
 (Buch) |
Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!
| Lieferstatus: |
Auf Bestellung (Lieferzeit unbekannt) |
| Veröffentlichung: |
August 2019
|
| Genre: |
Naturwissensch., Medizin, Technik |
|
|
Artificial Intelligence / Automation / B / Computational Intelligence / Computer Imaging, Vision, Pattern Recognition and Graphics / Computer Vision / Control, Robotics, Automation / engineering |
| ISBN: |
9789811392160 |
|
EAN-Code:
|
9789811392160 |
| Verlag: |
Springer EN |
| Einband: |
Gebunden |
| Sprache: |
English
|
| Dimensionen: |
H 235 mm / B 155 mm / D |
| Gewicht: |
694 gr |
| Seiten: |
328 |
| Illustration: |
XXII, 328 p. 99 illus., 78 illus. in color. With Jointly published with Xi'an Jiaotong University Press, Xi'an, China., farbige Illustrationen, schwarz-weiss Illustrationen |
| Zus. Info: |
EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. |
| Bewertung: |
Titel bewerten / Meinung schreiben
|
| Inhalt: |
| 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. |
|