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  4. Autonomous dirt detection for cleaning in office environments
 
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2013
Conference Paper
Title

Autonomous dirt detection for cleaning in office environments

Abstract
The advances of technologies for mobile robotics enable the application of robots to increasingly complex tasks. Cleaning office buildings on a daily basis is a problem that could be partially automatized with a cleaning robot that assists the cleaning professional yielding a higher cleaning capacity. A typical task in this domain is the selective cleaning, that is a focused cleaning effort to dirty spots, which speeds up the overall cleaning procedure significantly. To enable a robotic cleaner to accomplish this task, it is first necessary to distinguish dirty areas from the clean remainder. This paper discusses a vision-based dirt detection system for mobile cleaning robots that can be applied to any surface and dirt without previous training, that is fast enough to be executed on a mobile robot and which achieves high dirt recognition rates of 90% at an acceptable false positive rate of 45%. The paper also introduces a large database of real scenes which was used for the evaluation and is publicly available.
Author(s)
Bormann, Richard  
Weisshardt, Florian
Arbeiter, Georg
Fischer, Jan
Mainwork
IEEE International Conference on Robotics and Automation, ICRA 2013  
Project(s)
AutoPnP
Funder
Bundesministerium für Wirtschaft und Technolgie BMWi (Deutschland)  
Conference
International Conference on Robotics and Automation (ICRA) 2013  
File(s)
Download (3.69 MB)
Rights
Use according to copyright law
DOI
10.24406/publica-r-380090
10.1109/ICRA.2013.6630733
Language
English
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA  
Keyword(s)
  • Schmutzerkennung

  • dirt detection

  • robotic dirt detection

  • Bodenreinigungsroboter

  • Cleaning Robot

  • robotic cleaning

  • machine vision

  • maschinelles Sehen

  • Schmutzdatenbank

  • Roboter

  • Reinigen

  • Bilderkennung

  • Schmutz

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