A Low Cost Localization Solution Using a Kalman Filter for Data Fusion

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dc.contributor.authorKing, Peter Haywooden
dc.contributor.committeechairWicks, Alfred L.en
dc.contributor.committeememberReinholtz, Charles F.en
dc.contributor.committeememberHong, Dennis W.en
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2014-03-14T20:35:41Zen
dc.date.adate2008-06-06en
dc.date.available2014-03-14T20:35:41Zen
dc.date.issued2008-04-29en
dc.date.rdate2008-06-06en
dc.date.sdate2008-05-08en
dc.description.abstractPosition in the environment is essential in any autonomous system. As increased accuracy is required, the costs escalate accordingly. This paper presents a simple way to systematically integrate sensory data to provide a drivable and accurate position solution at a low cost. The data fusion is handled by a Kalman filter tracking five states and an undetermined number of asynchronous measurements. This implementation allows the user to define additional adjustments to improve the overall behavior of the filter. The filter is tested using a suite of inexpensive sensors and then compared to a differential GPS position. The output of the filter is indeed a drivable solution that tracks the reference position remarkably well. This approach takes advantage of the short-term accuracy of odometry measurements and the long-term fix of a GPS unit. A maximum error of two meters of deviation from the reference is shown for a complex path over two minutes and 100 meters long.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-05082008-095647en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05082008-095647/en
dc.identifier.urihttp://hdl.handle.net/10919/32384en
dc.publisherVirginia Techen
dc.relation.haspartPHKThesis.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectPositioningen
dc.subjectSignal Processingen
dc.subjectKalman Filteren
dc.subjectAutonomousen
dc.subjectGPSen
dc.titleA Low Cost Localization Solution Using a Kalman Filter for Data Fusionen
dc.typeThesisen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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