A new method for range estimation using simple infrared sensors

We describe a new method for position estimation of planar surfaces using simple, low-cost infrared (IR) sensors. The intensity data acquired with IR sensors depends 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 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 IR 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 the position of the maximum value of the intensity data rather than using absolute intensity values. 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. 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 IR sensors can be used for localization to an unexpectedly high accuracy without prior knowledge of the surface characteristics. © 2005 IEEE

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

Statistical pattern recognition techniques for target differentiation using infrared sensor

This study compares the performances of various statistical pattern recognition techniques for the differentiation of commonly encountered features in indoor environments, possibly with different surface properties, using simple infrared (IR) 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 differentiation process. We construct feature vectors based on the parameters of angular IR intensity scans from different targets to determine their geometry type. 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. The results indicate that the geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. The results demonstrate that simple IR sensors, when coupled with appropriate processing and recognition techniques, can be used to extract substantially more information than such devices are commonly employed for. © 2006 IEEE

Range estimation using simple infrared sensors without prior knowledge of surface parameters [Kizilberisi Algilayicilardan Elde Edilen Sinyallerle Yüzey Özelliklerinden Baǧimsiz Konum Kestirimi]

This paper describes a new method for range estimation using low-cost infrared sensors. The intensity data obtained with infrared sensors depends highly on the surface properties and the configuration of the sensors and the surface. Therefore, in many of the related studies, either the properties of the surface are determined first or certain assumptions about the surface are made in order to calculate the distance and the orientation of the surface relative to the sensors. In this paper, 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 verified experimentally with planar surfaces covered with white paper, wooden block, bubbled packing material, white styrofoam, blue and brown cardboard. The overall absolute mean error in the range estimates has been calculated as 0.21 cm in the range from 12.5 to 45 cm. The results obtained demonstrate that infrared sensors can be easily used for localization to an unexpectedly high accuracy without prior knowledge of the surface parameters. © 2004 IEEE

Target differentiation with infrared sensors using statistical pattern recognition techniques [İstatistiksel örüntü tanima teknikleri kullanarak kizilberisi algilayicilarla hedef ayirdetme]

This study compares the performances of different statistical pattern recognition techniques to differentiation of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders, using low-cost infrared sensors. The pattern recognition techniques compared include parametric density estimation, mixture of Gaussians, kernel estimator, k-nearest neighbor classifier, neural network classifier, and support vector machine classifier. A correct differentiation rate of 100% is achieved for six surfaces using parametric differentiation. For three geometries covered with seven different surfaces, best correct differentiation rate (100%) is achieved with mixture of Gaussians classifier with three components. 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. © 2006 IEEE