Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICRA.2013.6630733 | Robotics and Automation |
Keywords | Field | DocType |
cleaning,mobile robots,object detection,robot vision,autonomous dirt detection,cleaning robot,mobile robotics,office environments | Computer vision,Object detection,Robot vision,Control engineering,Real-time computing,Dirt,Artificial intelligence,Engineering,Robot,Mobile robot,Robotics | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1050-4729 |
ISBN | Citations | PageRank |
978-1-4673-5641-1 | 7 | 0.62 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richard Bormann | 1 | 43 | 6.01 |
| Florian Weisshardt | 2 | 39 | 5.26 |
| Georg Arbeiter | 3 | 56 | 7.09 |
| Fischer, J. | 4 | 13 | 1.74 |