Surface differentiation and localization by parametric modeling of infrared intensity scans
Author
Aytaç, Tayfun
Barshan, Billur
Date
2005Source Title
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2005
Publisher
IEEE
Pages
2294 - 2299
Language
English
Type
Conference PaperItem Usage Stats
143
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112
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Abstract
In this study, surfaces with different properties are differentiated with simple low-cost infrared (IR) emitters and detectors in a location-invariant manner. The intensity readings obtained from such sensors are highly dependent on the location and properties of the surface, which complicates the differentiation and localization process. Our approach, which models IR intensity scans parametrically, can distinguish different surfaces independent of their positions. The method is verified experimentally with wood, Styrofoam packaging material, white painted wall, white and black cloth, and white, brown, and violet paper. A correct differentiation rate of 100% is achieved for six surfaces and the surfaces are localized within absolute range and azimuth errors of 0.2 cm and 1.1°, respectively. The differentiation rate decreases to 86% for seven surfaces and to 73% for eight surfaces. The method demonstrated shows that simple IR sensors, when coupled with appropriate processing, can be used to differentiate different types of surfaces in a location-invariant manner.
Keywords
Surface differentiationInfrared sensors
Position estimation
Lambertian reflection
Feature extraction