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dc.contributor.advisorWebb, Barbara
dc.contributor.authorSzenher, Matthew D
dc.date.accessioned2009-11-20T14:40:40Z
dc.date.available2009-11-20T14:40:40Z
dc.date.issued2008
dc.identifier.urihttp://hdl.handle.net/1842/3193
dc.descriptionInstitute of Perception, Action and Behaviour
dc.description.abstractOur dissertation concerns robotic navigation in dynamic indoor environments using image-based visual homing. Image-based visual homing infers the direction to a goal location S from the navigator’s current location C using the similarity between panoramic images IS and IC captured at those locations. There are several ways to compute this similarity. One of the contributions of our dissertation is to identify a robust image similarity measure – mutual image information – to use in dynamic indoor environments. We crafted novel methods to speed the computation of mutual image information with both parallel and serial processors and demonstrated that these time-savers had little negative effect on homing success. Image-based visual homing requires a homing agent tomove so as to optimise themutual image information signal. As the mutual information signal is corrupted by sensor noise we turned to the stochastic optimisation literature for appropriate optimisation algorithms. We tested a number of these algorithms in both simulated and real dynamic laboratory environments and found that gradient descent (with gradients computed by one-sided differences) works best.en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.subjectInformaticsen
dc.subjectInstitute of Perception, Action and Behaviouren
dc.subjectRoboticsen
dc.subjectImage processingen
dc.titleVisual Homing in Dynamic Indoor Environmentsen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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