Context-based Selection and Execution of Robot Perception Graphs

Hochgeschwender, Nico; Olivares Mendez, Miguel Angel; Voos, Holger; Kraetzschmar, Gerhard K.

Reference : Context-based Selection and Execution of Robot Perception Graphs


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/22384

Title :	Context-based Selection and Execution of Robot Perception Graphs
Language :	English
Author, co-author :	Hochgeschwender, Nico [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC)]
	Olivares Mendez, Miguel Angel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Voos, Holger [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
	Kraetzschmar, Gerhard K. [> >]
Publication date :	Sep-2015
Main document title :	20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA'15)
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA'15)
Event date :	from 8-09-2015 to 11-09-2015
Keywords :	[en] knowledge representation ; computer vision ; illumination invariant
Abstract :	[en] To perform a wide range of tasks service robots need to robustly extract knowledge about the world from the data perceived through the robot’s sensors even in the presence of varying context-conditions. This makes the design and devel- opment of robot perception architectures a challenging exercise. In this paper we propose a robot perception architecture which enables to select and execute at runtime different perception graphs based on monitored context changes. To achieve this the architecture is structured as a feedback loop and contains a repository of different perception graph configurations suitable for various context conditions.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/22384

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