Rule-based target differentiation and position estimation based on infrared intensity measurements
dc.citation.epage | 1771 | en_US |
dc.citation.issueNumber | 6 | en_US |
dc.citation.spage | 1766 | en_US |
dc.citation.volumeNumber | 42 | en_US |
dc.contributor.author | AytaƧ, T. | en_US |
dc.contributor.author | Barshan, B. | en_US |
dc.date.accessioned | 2016-02-08T10:29:51Z | |
dc.date.available | 2016-02-08T10:29:51Z | |
dc.date.issued | 2003 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description.abstract | 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. | en_US |
dc.identifier.doi | 10.1117/1.1570428 | en_US |
dc.identifier.issn | 0091-3286 | |
dc.identifier.uri | http://hdl.handle.net/11693/24468 | |
dc.language.iso | English | en_US |
dc.publisher | SPIE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1117/1.1570428 | en_US |
dc.source.title | Optical Engineering | en_US |
dc.subject | Infrared sensors | en_US |
dc.subject | Optical sensing | en_US |
dc.subject | Pattern recognition and feature extraction | en_US |
dc.subject | Position estimation | en_US |
dc.subject | Target differentiation and localization | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Knowledge based systems | en_US |
dc.subject | Mathematical models | en_US |
dc.subject | Mobile robots | en_US |
dc.subject | Position control | en_US |
dc.subject | Robotic arms | en_US |
dc.subject | Surface properties | en_US |
dc.subject | Electronic stick | en_US |
dc.subject | Infrared intensity measurement | en_US |
dc.subject | Infrared sensor | en_US |
dc.subject | Phong illumination model | en_US |
dc.subject | Position estimation | en_US |
dc.subject | Rule-based target differentiation | en_US |
dc.subject | Infrared detectors | en_US |
dc.title | Rule-based target differentiation and position estimation based on infrared intensity measurements | en_US |
dc.type | Article | en_US |
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