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dc.contributor.advisorKarl Iagnemma.en_US
dc.contributor.authorHalatci, Ibrahimen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.date.accessioned2007-08-03T18:24:18Z
dc.date.available2007-08-03T18:24:18Z
dc.date.copyright2006en_US
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/38271
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.en_US
dc.descriptionIncludes bibliographical references (leaves 63-66).en_US
dc.description.abstractAutonomous rover operation plays a key role in planetary exploration missions. Rover systems require more and more autonomous capabilities to improve efficiency and robustness. Rover mobility is one of the critical components that can directly affect mission success. Knowledge of the physical properties of the terrain surrounding a planetary exploration rover can be used to allow a rover system to fully exploit its mobility capabilities. Here a study of multi-sensor terrain classification for planetary rovers in Mars and Mars-like environments is presented. Supervised classification algorithms for color, texture, and range features are presented based on mixture of Gaussians modeling. Two techniques for merging the results of these "low level" classifiers are presented that rely on Bayesian fusion and meta-classifier fusion. The performances of these algorithms are studied using images from NASA's Mars Exploration Rover mission and through experiments on a four-wheeled test-bed rover operating in Mars-analog terrain. It is shown that accurate terrain classification can be achieved via classifier fusion from visual features.en_US
dc.description.statementofresponsibilityby Ibrahim Halatci.en_US
dc.format.extent91 leavesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectMechanical Engineering.en_US
dc.titleVision-based terrain classification and classifier fusion for planetary exploration roversen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.identifier.oclc151219636en_US


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