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dc.contributor.authorWimpey, Bradley Joe
dc.date.accessioned2014-03-04T21:00:24Z
dc.date.available2014-03-04T21:00:24Z
dc.date.issued2012-12
dc.identifier.otherwimpey_bradley_j_201212_phd
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/wimpey_bradley_j_201212_phd
dc.identifier.urihttp://hdl.handle.net/10724/28631
dc.description.abstractFor visual approaches to simultaneous localization and mapping, the use of visual keypoint features is a popular choice. Certain areas of the environment, however, may contain ambiguous architecture or areas of repetition, which may cause keypoints to be matched well at multiple locations; it would be useful to consider other sources of evidence in addition to keypoints to make decisions. We propose a system that samples from varying layers of a location’s signature, from global or simple properties such as color and straight lines, to texture related characteristics in keypoints, to symbolic, human characteristics and semantic information such as text recognition and object detection and recognition. We propose how these visual feature "ridges" can be associated together and utilized to form our version of a visual "fingerprint" of a place, how these fingerprints can be compared for localization, and how they can be linked together to form a topological map of the environment. This new approach is called SPLINTR: Spatial Place Recognition in a Topologically Mapping Robot.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectrobotics
dc.subjectmapping
dc.subjectlocalization
dc.subjectmachine vision
dc.subjectSLAM
dc.titleSPLINTR: Spatial Place Recognition in a Topologically Mapping Robot
dc.typeDissertation
dc.description.degreePhD
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorWalter D. Potter
dc.description.committeeWalter D. Potter
dc.description.committeePrashant Doshi
dc.description.committeeSuchendra M. Bhandarkar


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