Browsing by Subject "Optical sensing"

Now showing 1 - 7 of 7

Open Access
A comparative analysis of different approaches to target differentiation and localization using infrared sensors
(2006) Aytaç, Tayfun
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.
Open Access
Improved range estimation using simple infrared sensors without prior knowledge of surface characteristics
(Institute of Physics Publishing Ltd., 2005) Yüzbaşioglu, Ç.; Barshan, B.
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.
Open Access
A new method for range estimation using simple infrared sensors
(IEEE, 2005-08) Yüzbaşıoǧlu, Çağrı; Barshan, Billur
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.
Open Access
Rule-based target differentiation and position estimation based on infrared intensity measurements
(SPIE, 2003) Aytaç, T.; Barshan, B.
This study investigates the use of low-cost infrared sensors in the differentiation and localization of target primitives commonly encountered in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings from such sensors are highly dependent on target location and properties in a way that cannot be represented in a simple manner, making the differentiation and localization difficult. We propose the use of angular intensity scans from two infrared sensors and present a rule-based algorithm to process them. The method can achieve position-invariant target differentiation without relying on the absolute return signal intensities of the infrared sensors. The method is verified experimentally. Planes, 90-deg corners, 90-deg edges, and cylinders are differentiated with correct rates of 90%, 100%, 82.5%, and 92.5%, respectively. Targets are localized with average absolute range and azimuth errors of 0.55 cm and 1.03 deg. The demonstration shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than they are commonly employed for.
Open Access
Simultaneous extraction of geometry and surface properties of targets using simple infrared sensors
(SPIE, 2004) Aytaç, T.; Barshan, B.
We investigate the use of low-cost infrared (IR) sensors for the simultaneous extraction of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, and edges. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and recognition process. We propose the use of angular intensity scans and present an algorithm to process them to determine the geometry and the surface type of the target and estimate its position. The method is verified experimentally with planes, 90-deg corners, and 90-deg edges covered with aluminum, white cloth, and Styrofoam packaging material. An average correct classification rate of 80% of both geometry and surface over all target types is achieved and targets are localized within absolute range and azimuth errors of 1.5 cm and 1.1 deg, respectively. Taken separately, the geometry and surface type of targets can be correctly classified with rates of 99 and 81%, respectively, which shows that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor. The method demonstrated shows that simple IR sensors, when coupled with appropriate processing, can be used to extract substantially more information than that for which such devices are commonly employed. © 2004 Society of Photo-Optical Instrumentation Engineers.
Open Access
Surface differentiation by parametric modeling of infrared intensity scans
(SPIE-International Society for Optical Engineering, 2005) Aytaç, T.; Barshan, B.
We differentiate surfaces with different properties with simple low-cost IR emitters and detectors in a location-invariant manner. The intensity readings obtained with 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. Once the surface type is identified, its position (r, θ) can also be estimated. The method is verified experimentally with wood; Styrofoam packaging material; white painted matte 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 deg, 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 signal processing, can be used to recognize different types of surfaces in a location-invariant manner.
Open Access
Target differentiation with simple infrared sensors using statistical pattern recognition techniques
(Elsevier BV, 2007) Barshan, B.; Aytaç, T.; Yüzbaşıoğlu, Ç.
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 and/or surface 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. Parametric differentiation correctly identifies six different surface types of the same planar geometry, resulting in the best surface differentiation rate (100%). However, this rate is not maintained with the inclusion of more surfaces. 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.