Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Autonomous dirt detection for cleaning in office environments

: Bormann, Richard; Weisshardt, Florian; Arbeiter, Georg; Fischer, Jan

Preprint urn:nbn:de:0011-n-2421004 (3.6 MByte PDF)
MD5 Fingerprint: 1faf20ca6f1677f1fe1b4a7af895411e
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 30.11.2013

IEEE Robotics and Automation Society; Karlsruher Institut für Technologie -KIT-:
IEEE International Conference on Robotics and Automation, ICRA 2013 : Anthropomatics - Technologies for Humans. May 6-10, 2013, Karlsruhe, Germany
New York, NY: IEEE, 2013
ISBN: 978-1-4673-5642-8 (USB Stick)
ISBN: 978-1-4673-5641-1 (Print)
ISBN: 978-1-4673-5643-5 (electronic)
ISBN: 978-1-4673-5640-4 (print)
International Conference on Robotics and Automation (ICRA) <2013, Karlsruhe>
Bundesministerium für Wirtschaft und Technologie BMWi
01Ma11005; AutoPnP
Conference Paper, Electronic Publication
Fraunhofer IPA ()
Schmutzerkennung; dirt detection; robotic dirt detection; Bodenreinigungsroboter; Cleaning Robot; robotic cleaning; machine vision; maschinelles Sehen; Schmutzdatenbank; Roboter; Reinigen; Bilderkennung; Schmutz

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.