Improved range estimation using simple infrared sensors without prior knowledge of surface characteristics

Cataloged from PDF version of article.This thesis describes a new method for range estimation using low-cost infrared sensors. The intensity data acquired with infrared sensors depends highly on the surface properties and the con guration of the sensors with respect to the surface. Therefore, in many related studies, either the properties of the surface are determined rst or certain assumptions about the surface are made in order to estimate the distance and the orientation of the surface relative to the sensors. We propose a novel method for position estimation of surfaces with infrared sensors without the need to determine the surface properties rst. The method is relatively independent of the type of surface encountered since it is based on searching the maximum value of the intensity rather than using absolute intensity values for a given surface which would depend on the surface type. The method is veri ed experimentally with planar surfaces of di erent surface properties. An intelligent feature of our system is that its operating range is made adaptive based on the intensity of the detected signal. Three di erent ways of processing the intensity signals are considered for range estimation. The overall absolute mean error in the range estimates has been calculated as 0.15 cm in the range from 10 to 50 cm. The cases where the azimuth and elevation angles are nonzero are considered as well. The results obtained demonstrate that infrared sensors can be used for localization to an unexpectedly high accuracy without prior knowledge of the surface characteristics.Yüzbaşıoğlu, R. ÇağrıM.S

Improved range estimation using simple infrared sensors without prior knowledge of surface characteristics

This paper describes a new method for position estimation of planar surfaces using simple, low-cost infrared sensors. The intensity data acquired with infrared sensors depend highly on the surface properties and the configuration of the sensors with respect to the surface. Therefore, in many related studies, either the properties of the surface are determined first or certain assumptions about the surface are made in order to estimate the distance and the orientation of the surface relative to the sensors. We propose a novel method for position estimation of surfaces with infrared sensors without the need to determine the surface properties first. The method is considered to be independent of the type of surface encountered since it is based on searching for the position of the maximum value of the intensity data rather than using absolute intensity values which would depend on the surface type. The method is verified experimentally with planar surfaces of different surface properties. An intelligent feature of our system is that its operating range is made adaptive based on the maximum intensity of the detected signal. Three different ways of processing the intensity signals are considered for range estimation. The absolute mean range error for the method resulting in the lowest errors is 0.15 cm over the range from 10 to 50 cm. The cases where the azimuth and elevation angles are nonzero are considered as well. The results obtained demonstrate that infrared sensors can be used for localization to an unexpectedly high accuracy without prior knowledge of the surface characteristics. © 2005 IOP Publishing Ltd

A comparative analysis of different approaches to target differentiation and localization using infrared sensors

Cataloged from PDF version of article.This study compares the performances of various techniques for the differentiation and localization of commonly encountered features in indoor environments, such as planes, corners, edges, and cylinders, possibly with different surface properties, using simple infrared sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and differentiation process. The techniques considered include rule-based, template-based, and neural network-based target differentiation, parametric surface differentiation, and statistical pattern recognition techniques such as parametric density estimation, various linear and quadratic classifiers, mixture of normals, kernel estimator, k-nearest neighbor, artificial neural network, and support vector machine classi- fiers. The geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. For a set of six surfaces, we get a correct differentiation rate of 100% in parametric differentiation based on reflection modeling. The results demonstrate that simple infrared sensors, when coupled with appropriate processing, can be used to extract substantially more information than such devices are commonly employed for. The demonstrated system would find application in intelligent autonomous systems such as mobile robots whose task involves surveying an unknown environment made of different geometry and surface types. Industrial applications where different materials/surfaces must be identified and separated may also benefit from this approach.Aytaç, TayfunPh.D