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dc.contributor.advisorMichael E. Cleary.en_US
dc.contributor.authorFenwick, John William, 1977-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2005-08-23T21:24:05Z
dc.date.available2005-08-23T21:24:05Z
dc.date.copyright2001en_US
dc.date.issued2001en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/8575
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractAutonomous vehicles require the ability to build maps of an unknown environment while concurrently using these maps for navigation. Current algorithms for this concurrent mapping and localization (CML) problem have been implemented for single vehicles, but do not account for extra positional information available when multiple vehicles operate simultaneously. Multiple vehicles have the potential to map an environment more quickly and robustly than a single vehicle. This thesis presents a collaborative CML algorithm that merges sensor and navigation information from multiple autonomous vehicles. The algorithm presented is based on stochastic estimation and uses a feature-based approach to extract landmarks from the environment. The theoretical framework for the collaborative CML algorithm is presented, and a convergence theorem central to the cooperative CML problem is proved for the first time. This theorem quantifies the performance gains of collaboration, allowing for determination of the number of cooperating vehicles required to accomplish a task. A simulated implementation of the collaborative CML algorithm demonstrates substantial performance improvement over non-cooperative CML.en_US
dc.description.statementofresponsibilityby John William Fenwick.en_US
dc.format.extent130 p.en_US
dc.format.extent7000951 bytes
dc.format.extent7000708 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
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.subjectElectrical Engineering and Computer Science.en_US
dc.titleCollaborative concurrent mapping and localizationen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc49223496en_US


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