Gillham, Michael, Howells, Gareth, Spurgeon, Sarah K., McElroy, Ben (2013) Floor Covering and Surface Identification for Assistive Mobile Robotic Real-Time Room Localization Application. Sensors, 13 (12). pp. 17501-17515. ISSN 1424-8220. (doi:10.3390/s131217501) (KAR id:38133)
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Official URL: http://dx.doi.org/10.3390/s131217501 |
Abstract
Assistive robotic applications require systems capable of interaction in the human world, a workspace which is highly dynamic and not always predictable. Mobile assistive devices face the additional and complex problem of when and if intervention should occur; therefore before any trajectory assistance is given, the robotic device must know where it is in real-time, without unnecessary disruption or delay to the user requirements. In this paper, we demonstrate a novel robust method for determining room identification from floor features in a real-time computational frame for autonomous and assistive robotics in the human environment. We utilize two inexpensive sensors: an optical mouse sensor for straightforward and rapid, texture or pattern sampling, and a four color photodiode light sensor for fast color determination. We show how data relating floor texture and color obtained from typical dynamic human environments, using these two sensors, compares favorably with data obtained from a standard webcam. We show that suitable data can be extracted from these two sensors at a rate 16 times faster than a standard webcam, and that these data are in a form which can be rapidly processed using readily available classification techniques, suitable for real-time system application. We achieved a 95% correct classification accuracy identifying 133 rooms’ flooring from 35 classes, suitable for fast coarse global room localization application, boundary crossing detection, and additionally some degree of surface type identification.
Item Type: | Article |
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DOI/Identification number: | 10.3390/s131217501 |
Uncontrolled keywords: | mobile robotics; floor features; optical mouse; room localization |
Subjects: |
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.P3 Pattern recognition systems T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7895.E42 Embedded computer systems T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK8300 Optoelectronic devices. Photoelectronic devices |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | Michael Gillham |
Date Deposited: | 01 Feb 2014 19:10 UTC |
Last Modified: | 05 Nov 2024 10:22 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/38133 (The current URI for this page, for reference purposes) |
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