{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/8575"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/8575","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Collaborative concurrent mapping and localization","abstract":"Autonomous 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.","abstract_html":"Autonomous 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.","abstract_has_math":false,"creators":["Fenwick, John William, 1977-"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Michael E. Cleary."],"committee_chairs":[],"committee_members":[],"year":2001,"date_issued":"2001","date_published":"2001","updated_at":"2026-07-22T22:22:30Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/8575","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Michael E. Cleary."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Fenwick, John William, 1977-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2005-08-23T21:24:05Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2005-08-23T21:24:05Z"]},{"key":"dc:date.issued","label":"Date","values":["2001"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["M.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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/8575"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.","Includes bibliographical references."]},{"key":"dc:description.abstract","label":"Abstract","values":["Autonomous 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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Collaborative concurrent mapping and localization"]}]}],"canonical_facts":{"dc:contributor.advisor":["Michael E. Cleary."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Fenwick, John William, 1977-"],"dc:date.accessioned":["2005-08-23T21:24:05Z"],"dc:date.available":["2005-08-23T21:24:05Z"],"dc:date.issued":["2001"],"dc:description":["Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.","Includes bibliographical references."],"dc:description.abstract":["Autonomous 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."],"dc:description.degree":["S.M."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/8575"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Collaborative concurrent mapping and localization"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:22:30Z"}