Cataloged from PDF version of article.This study investigates the use of low-cost infrared emitters and detectors in the differentiation and localization of
commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders. The
intensity readings obtained with such systems are highly dependent on target location and properties in a way which
cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we
propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the
target type independent of its position. Once the target type is identified, its position can also be estimated. The method is
verified experimentally. An average correct classification rate of 97% over all target types is achieved and targets are
localized within absolute range and azimuth errors of 0.8 cm and 1.6 , respectively. The method demonstrated shows that
simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount
of information than that which they are commonly employed for.  (C) 2002 Elsevier Science B.V. All rights reserve

Aytac, T.

Barshan, B.

Optics Communications

Bilkent University Institutional Repository

Differentiation and localization of targets using infrared sensors

This study investigates the use of low-cost infrared emitters and detectors in the differentiation and localization of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings obtained with such systems are highly dependent on target location and properties in a way which cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the target type independent of its position. Once the target type is identified, its position can also be estimated. The method is verified experimentally. An average correct classification rate of 97 % over all target types is achieved and targets are localized within absolute range and azimuth errors of 0.8 cm and 1.6, respectively. The method demonstrated shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than that which they are commonly employed for.  2002 Elsevier Science B.V. All rights reserved

Tayfun Aytac

Billur Barshan

CiteSeerX

This study investigates the use of low-cost infrared sensors in the differentiation and localization of commonly encountered target primitives 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 which cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the target type independent of its position. Once the target type is identified, its position can also be estimated. The method is verified experimentally. An average correct classification rate of 97% over all target types is achieved and targets are localized within absolute range and azimuth errors of 0.8 cm and 1.6°, respectively. The proposed method should facilitate the use of infrared sensors in mobile robot applications for differentiation and localization beyond their common usage as simple proximity sensors for object detection and collision avoidance

Aytaç, T.

Differentiation and localization of target primitives using infrared sensors

We discuss the use of low-cost infrared sensors in differentiating and localizing commonly encountered target primitives in indoor environments, such as planes, corners, edges, and cylinders. Single intensity readings are highly dependent on target location and properties and this dependence cannot be represented simply. We propose a method that can achieve position-invariant target differentiation without relying on absolute intensity readings and verify it experimentally. The correct identification rates for planes, 90° corners and edges, and cylinders are 90%, 100%, 82.5%, and 92.5%, respectively. The distance of the target can be estimated with an average error of 0.59 cm and the azimuth angle can be estimated with an error of 1.58°

Target differentiation and localizationusing infrared sensorsTayfun Aytaç and Billur BarshanDepartrrierit of Electrical and Electronics EngineeringBilkent University, Bilkent, TR-06533 Ankara, TurkeyE-mail: {taytac, billur}©ee.bilkent.edu.trABSTRACTWe discuss the use of low-cost infrared sensors in differentiating and localizing commonly encountered targetprimitives in indoor environments, such as planes, corners, edges, and cylinders. Single intensity readingsare highly dependent on target location and properties and this dependence cannot be represented simply. Wepropose a method that can achieve position-invariant target differentiation without relying on absolute intensityreadings and verify it experimentally. The correct identification rates for planes, 900 corners and edges, andcylinders are 90%, 100%, 82.5%, and 92.5%, respectively. The distance of the target can be estimated with anaverage error of 0.59 ciii arid the azimuth angle caii be estimated with an error of 1.58°.1 . INTRODUCTIONInfrared sensors are inexpensive, practical and widely available devices. However, it is difficult to make reliabledistance estimates based on the value of a single intensity return because the return depends on both thegeometry and the surface properties of the reflecting target. Likewise, the geometry or surface properties cannotbe deduced from simple intensity returns without knowing the distance and angular location of the target. Inthis paper, we propose a scanning technique and an algorithm that can determine the type of the target in amanner which is invariant to its location. Once the target type is determined, its distance and azimuth anglecan be estimated. Target identification is of great interest especially in robotics applications, where there is needto identify targets in mobile robot environments for autonomous operation. Typical applications of infraredsensors in this area are mainly floor sensing, navigational referencing, and collision avoidance at shortInfrared sensors have been used to locate edges of doorways in mobile robot navigation.2 In another study, thesurface properties of an object at a known distance have been determined using the Phong model, and usingthis information, the infrared sensor employed has been modeled as an accurate range finder for surfaces atshort ranges3 (3—25 cm). However, to the best of our knowledge, no attempt has been made to differentiatetargcts using infrared sensors.2. THE METHODThe infrared sensor4 used in this study works with 20—28 V DC input voltage, and provides analog output voltageproportional to the measured intensity between ranges 0—60 cm. The detector window is covered with an infraredfilter to minimize the effect of ambient light on the intensity measurements. First, we have attempted to achievetarget differentiation with a single infrared sensor. Our method is based on angularly scanning the target over acertain angular range. We have shown that the resulting angular intensity scans contain sufficient informationto identify several different target types and estimate their distance and azimuth. Reference data sets arecollected for each target with 2.5 cm distance increments, making OO with the line-of-sight of the experimentalsetup. We tested our method by locating the targets at randomly selected distances and azimuth angles andcollecting 120 such test scans. With this approach, 93% average correct classificatioii rate, arid distance andazimuth estimation errors of 0.59 cm and 1.69° are achieved. However, with the use of a single sensor, it wouldbe necessary to store reference data for different types of surfaces. To remedy this, we considered the use of twosensors mounted on a 12 inch rotary table horizontally, with a center-to-center separation of 11 cm. Typicalscan patterns for this configuration are shown in Fig 1. Based on these patterns, it is observed that the returnsignal intensity patterns for a corner, which have two maxima and a single minimum, differ significantly fromthose of other targets which have a single maximum [Fig. la]. Because of these distinctive characteristics, thecorner differeiitiatioii rule is first employed. If the target is foumid riot to be a corner, we next check whether it*Corresponding Author.19th Congress of the International Commission for Optics:Optics for the Quality of Life, Giancarlo C. Righini, Anna Consortini,Editors, SPIE Vol. 4829 (2003) © 2003 SPIE · 0277-786X/03/$15.00889Downloaded From: https://www.spiedigitallibrary.org/conference-proceedings-of-spie on 9/28/2017 Terms of Use: https://spiedigitallibrary.spie.org/ss/TermsOfUse.aspxr= 25cm / Right) ———LeftI i r35cmr3Ocmii r4Ocmr= 35cm j2: —2" o 60 O—4O —20 0 20 40 60SCAN ANGLE (deg) SCAN ANGLE (deg)(c) (d)Figure 1. Intensity vs. scan angle characteristics for various targets making OO with the line-of-sight of the experimentalsetup: (a) corner, (b) plane, (c) edge, and (d) cylinder.is a plane by comparing the absolute difference of angle values at which the two intensity patterns have theirmaxima as shown in Fig. 1 .a. If the target is also not a plane, to check whether it is an edge or a cylinder, weuse the ratio of the maximum intensity to the intensity value at the intersection point, if the patterns intersectIFig. 1 .c aiid d]. If the two intensity patterns do not intersect, the target is classified as undetermined, althoughthis situation did not arise in our tests.3. RESULTSWith the experimental setup described in Section 2, the algorithm is tested with a plane, 900 corner and edge,and a cylinder of radius 48 mm using 160 test cases. A target confusion matrix, which displays the informationabout the actual and detected targets by the sensors, is presented in Table 1. The algorithm differentiatescorners with 100% accuracy. Furthermore, when the target is not a corner, it is never classified as a corner.Table 1: Target confusion matrix. P:plane, C:corner, E:edge, CY:cylinder, U:undetermined.TARGET DIFFERENTIATION RESULT TOTALP C E CY UP 36 - 4 - - 40C 0 40 - - - 40E 4 - 33 3 - 40CY 3 - - 37 - 40U - - - - - -TOTAL 43 40 37 40 - 1604. CONCLUSIONThe accomplishment of this study is that even though the intensity patterns are highly dependent on targetrange and azimuth, and this dependence cannot be represented by a simple relationship, by developing a methodthat does not rely on absolute intensity values, we effectively achieve position-invariant target differentiation.An average correct target differentiation rate of 91% is achieved.REFERENCES1. H.R. Everett, Sensors for Mobile Robots, Theory and Application (A K Peters, Wellesley, 1995).2. A.M. Flynn, mt. J. Robot. Res., 17, 6, pp.598 623 (1988).3. P.M. Novotny and N.J. Ferrier, Proc. IEEE Tnt. Conf. Robot. Automat., 2, pp.1644 1649 (1999).4. IRS-U-4A/20-28 V, Datasheet Matrix Elektronik AG, Kirchweg 24 CH-5422 Oberehreiidingeri, Switzerlaiid.8r= 30cm8>62I1I,I,iiiiii1II1ii I'Ii,1890     Proc. of SPIE Vol. 4829Downloaded From: https://www.spiedigitallibrary.org/conference-proceedings-of-spie on 9/28/2017 Terms of Use: https://spiedigitallibrary.spie.org/ss/TermsOfUse.aspx

Target differentiation and localization using infrared sensors

This study investigates the use of low-cost infrared emitters and detectors in the differentiation and localization of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings obtained with such systems are highly dependent on target location and properties in a way which cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the target type independent of its position. Once the target type is identified, its position can also be estimated. The method is verified experimentally. An average correct classification rate of 97% over all target types is achieved and targets are localized within absolute range and azimuth errors of 0.8 cm and 1.6°, respectively. The method demonstrated shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than that which they are commonly employed for

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Differentiation and localization of targets using infrared sensors

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Bilkent University Institutional Repository