{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/95582"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/95582","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Monocular vision based navigation using image moments of polygonal features","abstract":"This thesis presents a novel monocular-vision-based localization and mapping algorithm using moments of polygon features. The landmarks we use are polygonal regions instead of a dense set of feature points, which can significantly reduce the computational complexity of data association and produce a map that is geometrically and structurally more meaningful. Each region can be characterized using its depth and orientation with respect to the camera and an polygon detection and tracking algorithm is developed. The monocular vision Simultaneous Localization and Mapping (SLAM) problem is formulated as a filter problem to incorporate the image moments of the close regions or polygons tracked. The observability of the SLAM estimator is further improved by both the additional measurements with respect to the initial view location and the use of image moments. We analyze the performance of our SLAM algorithm with numerical simulations and experimental results. We also compared our results with ORB-SLAM to show the effectiveness of our algorithm in outdoor environments.","abstract_html":"This thesis presents a novel monocular-vision-based localization and mapping algorithm using moments of polygon features. The landmarks we use are polygonal regions instead of a dense set of feature points, which can significantly reduce the computational complexity of data association and produce a map that is geometrically and structurally more meaningful. Each region can be characterized using its depth and orientation with respect to the camera and an polygon detection and tracking algorithm is developed. The monocular vision Simultaneous Localization and Mapping (SLAM) problem is formulated as a filter problem to incorporate the image moments of the close regions or polygons tracked. The observability of the SLAM estimator is further improved by both the additional measurements with respect to the initial view location and the use of image moments. We analyze the performance of our SLAM algorithm with numerical simulations and experimental results. We also compared our results with ORB-SLAM to show the effectiveness of our algorithm in outdoor environments.","abstract_has_math":false,"creators":["Ma, Lingyu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Hutchinson, Seth","Chung, Soon-Jo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-03-01T17:01:29Z","date_published":"2017-03-01T17:01:29Z","updated_at":"2026-07-22T22:26:37Z","subjects":["Simultaneous localization and mapping (SLAM)","Vision-based navigation"],"languages":["en"],"rights":["Copyright 2016 Lingyu Ma"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/95582","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hutchinson, Seth","Chung, Soon-Jo"]},{"key":"dc:creator","label":"Author","values":["Ma, Lingyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-03-01T17:01:29Z","2019-03-02T10:15:33Z","2016-11-23","2016-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Simultaneous localization and mapping (SLAM)","Vision-based navigation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Lingyu Ma"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/95582"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis presents a novel monocular-vision-based localization and mapping algorithm using moments of polygon features. The landmarks we use are polygonal regions instead of a dense set of feature points, which can significantly reduce the computational complexity of data association and produce a map that is geometrically and structurally more meaningful. Each region can be characterized using its depth and orientation with respect to the camera and an polygon detection and tracking algorithm is developed. The monocular vision Simultaneous Localization and Mapping (SLAM) problem is formulated as a filter problem to incorporate the image moments of the close regions or polygons tracked. The observability of the SLAM estimator is further improved by both the additional measurements with respect to the initial view location and the use of image moments. We analyze the performance of our SLAM algorithm with numerical simulations and experimental results. We also compared our results with ORB-SLAM to show the effectiveness of our algorithm in outdoor environments.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-12-01","The student, Lingyu Ma, accepted the attached license on 2016-11-21 at 19:12.","The student, Lingyu Ma, submitted this Thesis for approval on 2016-11-21 at 19:23.","This Thesis was approved for publication on 2016-11-23 at 13:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10290 on 2017-02-28 at 14:41:56","Made available in DSpace on 2017-03-01T17:01:29Z (GMT). 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The landmarks we use are polygonal regions instead of a dense set of feature points, which can significantly reduce the computational complexity of data association and produce a map that is geometrically and structurally more meaningful. Each region can be characterized using its depth and orientation with respect to the camera and an polygon detection and tracking algorithm is developed. The monocular vision Simultaneous Localization and Mapping (SLAM) problem is formulated as a filter problem to incorporate the image moments of the close regions or polygons tracked. The observability of the SLAM estimator is further improved by both the additional measurements with respect to the initial view location and the use of image moments. We analyze the performance of our SLAM algorithm with numerical simulations and experimental results. We also compared our results with ORB-SLAM to show the effectiveness of our algorithm in outdoor environments.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-12-01","The student, Lingyu Ma, accepted the attached license on 2016-11-21 at 19:12.","The student, Lingyu Ma, submitted this Thesis for approval on 2016-11-21 at 19:23.","This Thesis was approved for publication on 2016-11-23 at 13:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10290 on 2017-02-28 at 14:41:56","Made available in DSpace on 2017-03-01T17:01:29Z (GMT). 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