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dc.contributor.authorRiggins, Jamie N.en_US
dc.date.accessioned2014-03-14T20:38:34Z
dc.date.available2014-03-14T20:38:34Z
dc.date.issued2006-05-12en_US
dc.identifier.otheretd-05252006-170303en_US
dc.identifier.urihttp://hdl.handle.net/10919/33227
dc.description.abstractAs the mission field for autonomous vehicles expands into a larger variety of territories, the development of autonomous surface vehicles (ASVs) becomes increasingly important. ASVs have the potential to travel for long periods of time in areas that cannot be reached by aerial, ground, or underwater autonomous vehicles. ASVs are useful for a variety of missions, including bathymetric mapping, communication with other autonomous vehicles, military reconnaissance and surveillance, and environmental data collecting. Critical to an ASV's ability to maneuver without human intervention is its ability to detect obstacles, including the shoreline. Prior topological knowledge of the environment is not always available or, in dynamic environments, reliable. While many existing obstacle detection systems can only detect 3D obstacles at close range via a laser or radar signal, vision systems have the potential to detect obstacles both near and far, including "flat" obstacles such as the shoreline. The challenge lies in processing the images acquired by the vision system and extracting useful information. While this thesis does not address the issue of processing the images to locate the pixel positions of the obstacles, we assume that we have these processed images available. We present an algorithm that takes these processed images and, by incorporating the kinematic model of the ASV, maps the pixel locations of the obstacles into a global coordinate system. An Extended Kalman Filter is used to localize the ASV and the surrounding obstacles.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartthesis.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectfeature localizationen_US
dc.subjectomni-directional cameraen_US
dc.subjectExtended Kalman Filteren_US
dc.subjectautonomous surface vehicleen_US
dc.titleLocation Estimation of Obstacles for an Autonomous Surface Vehicleen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.committeechairStilwell, Daniel J.en_US
dc.contributor.committeememberBaumann, William T.en_US
dc.contributor.committeememberWyatt, Christopher L.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05252006-170303/en_US
dc.date.sdate2006-05-25en_US
dc.date.rdate2006-07-06
dc.date.adate2006-07-06en_US


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