Floor Covering and Surface Identification for Assistive Mobile Robotic Real-Time Room Localization Application
| Autor: | Sarah K. Spurgeon, Michael Gillham, Ben McElroy, Gareth Howells |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Computer science
TK7882.P3 Workspace room localization User requirements document lcsh:Chemical technology floor features Biochemistry Article Analytical Chemistry law.invention TK8300 Sampling (signal processing) law optical mouse Computer vision lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Simulation business.industry Frame (networking) Atomic and Molecular Physics and Optics Identification (information) TK7895.E42 mobile robotics Optical mouse Face (geometry) Trajectory Artificial intelligence business |
| Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 13 Issue 12 Pages 17501-17515 Sensors, Vol 13, Iss 12, Pp 17501-17515 (2013) |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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