Surface recognition by parametric modeling of infrared intensity signals

In this study, low-cost infrared emitters and detectors are used for the recognition of surfaces with different properties in a location-invariant manner. The intensity readings obtained with such sensors are highly dependent on the location and properties of the surface in a way that cannot be represented analytically in a simple manner, complicating the differentiation and localization process. Our approach, which models infrared intensity signals parametrically, can distinguish different surfaces independently of their positions. Once the surface type is identified, its position can also be estimated. The method is verified experimentally with wood, styrofoam packaging material, white painted wall, white and black clothes, and white, brown, and violet papers. A correct differentiation rate of 73% is achieved over eight surfaces and the surfaces are localized within absolute range and azimuth errors of 0.8 cm and 1.1°, respectively. The differentiation rate improves to 86% over seven surfaces and 100% over six surfaces. The method demonstrated shows that simple infrared sensors, when coupled with appropriate signal processing, can be used to extract a significantly greater amount of information than they are commonly employed for.