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Indoor scene recognition by 3-D obje...

MeiBner, Pascal.

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Indoor scene recognition by 3-D object search = for robot programming by demonstration /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Indoor scene recognition by 3-D object search/ by Pascal MeiBner.
Reminder of title:	for robot programming by demonstration /
Author:	MeiBner, Pascal.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xix, 262 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- RelatedWork -- PassiveSceneRecognition -- ActiveSceneRecognition -- Evaluation -- Summary -- Appendix.
Contained By:	Springer eBooks
Subject:	Robots - Programming. -
Online resource:	https://doi.org/10.1007/978-3-030-31852-9
ISBN:	9783030318529

Indoor scene recognition by 3-D object search = for robot programming by demonstration /
MeiBner, Pascal.

Indoor scene recognition by 3-D object searchfor robot programming by demonstration /[electronic resource] :by Pascal MeiBner. - Cham :Springer International Publishing :2020. - xix, 262 p. :ill. (some col.), digital ;24 cm. - Springer tracts in advanced robotics,v.1351610-7438 ;. - Springer tracts in advanced robotics ;v.135..

Introduction -- RelatedWork -- PassiveSceneRecognition -- ActiveSceneRecognition -- Evaluation -- Summary -- Appendix.

This book focuses on enabling mobile robots to recognize scenes in indoor environments, in order to allow them to determine which actions are appropriate at which points in time. In concrete terms, future robots will have to solve the classification problem represented by scene recognition sufficiently well for them to act independently in human-centered environments. To achieve accurate yet versatile indoor scene recognition, the book presents a hierarchical data structure for scenes - the Implicit Shape Model trees. Further, it also provides training and recognition algorithms for these trees. In general, entire indoor scenes cannot be perceived from a single point of view. To address this problem the authors introduce Active Scene Recognition (ASR), a concept that embeds canonical scene recognition in a decision-making system that selects camera views for a mobile robot to drive to so that it can find objects not yet localized. The authors formalize the automatic selection of camera views as a Next-Best-View (NBV) problem to which they contribute an algorithmic solution, which focuses on realistic problem modeling while maintaining its computational efficiency. Lastly, the book introduces a method for predicting the poses of objects to be searched, establishing the otherwise missing link between scene recognition and NBV estimation.

ISBN: 9783030318529

Standard No.: 10.1007/978-3-030-31852-9doiSubjects--Topical Terms:

656753
Robots
--Programming.

LC Class. No.: TJ211.45

Dewey Class. No.: 629.892

Indoor scene recognition by 3-D object search = for robot programming by demonstration /
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505 0 $a Introduction -- RelatedWork -- PassiveSceneRecognition -- ActiveSceneRecognition -- Evaluation -- Summary -- Appendix.
520 $a This book focuses on enabling mobile robots to recognize scenes in indoor environments, in order to allow them to determine which actions are appropriate at which points in time. In concrete terms, future robots will have to solve the classification problem represented by scene recognition sufficiently well for them to act independently in human-centered environments. To achieve accurate yet versatile indoor scene recognition, the book presents a hierarchical data structure for scenes - the Implicit Shape Model trees. Further, it also provides training and recognition algorithms for these trees. In general, entire indoor scenes cannot be perceived from a single point of view. To address this problem the authors introduce Active Scene Recognition (ASR), a concept that embeds canonical scene recognition in a decision-making system that selects camera views for a mobile robot to drive to so that it can find objects not yet localized. The authors formalize the automatic selection of camera views as a Next-Best-View (NBV) problem to which they contribute an algorithmic solution, which focuses on realistic problem modeling while maintaining its computational efficiency. Lastly, the book introduces a method for predicting the poses of objects to be searched, establishing the otherwise missing link between scene recognition and NBV estimation.
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