{"id":{"repo_id":"woods-hole","oai_identifier":"oai:darchive.mblwhoilibrary.org:1912/7136"},"canonical_url":"https://search.dev.ndltd.org/etd/woods-hole/oai:darchive.mblwhoilibrary.org:1912/7136","repository":{"repo_id":"woods-hole","name":"Woods Hole Oceanographic Institute","base_url":"https://darchive.mblwhoilibrary.org/server/oai/request"},"display":{"title":"Toward autonomous underwater mapping in partially structured 3D environments","abstract":"Motivated by inspection of complex underwater environments, we have developed a system for multi-sensor SLAM utilizing both structured and unstructured environmental features. We present a system for deriving planar constraints from sonar data, and jointly optimizing the vehicle and plane positions as nodes in a factor graph. We also present a system for outlier rejection and smoothing of 3D sonar data, and for generating loop closure constraints based on the alignment of smoothed submaps. Our factor graph SLAM backend combines loop closure constraints from sonar data with detections of visual fiducial markers from camera imagery, and produces an online estimate of the full vehicle trajectory and landmark positions. We evaluate our technique on an inspection of a decomissioned aircraft carrier, as well as synthetic data and controlled indoor experiments, demonstrating improved trajectory estimates and reduced reprojection error in the final 3D map.","abstract_html":"Motivated by inspection of complex underwater environments, we have developed a system for multi-sensor SLAM utilizing both structured and unstructured environmental features. We present a system for deriving planar constraints from sonar data, and jointly optimizing the vehicle and plane positions as nodes in a factor graph. We also present a system for outlier rejection and smoothing of 3D sonar data, and for generating loop closure constraints based on the alignment of smoothed submaps. Our factor graph SLAM backend combines loop closure constraints from sonar data with detections of visual fiducial markers from camera imagery, and produces an online estimate of the full vehicle trajectory and landmark positions. We evaluate our technique on an inspection of a decomissioned aircraft carrier, as well as synthetic data and controlled indoor experiments, demonstrating improved trajectory estimates and reduced reprojection error in the final 3D map.","abstract_has_math":false,"creators":["VanMiddlesworth, Mark"],"institution":"Massachusetts Institute of Technology and Woods Hole Oceanographic Institution","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-02","date_published":"2014-02","updated_at":"2026-07-27T22:05:10Z","subjects":["Underwater acoustics","Underwater acoustic telemetry"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.1575/1912/7136"],"render_values":[{"text":"10.1575/1912/7136","href":"https://doi.org/10.1575/1912/7136","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1912/7136","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["VanMiddlesworth, Mark"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-02-10T14:22:30Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-02-10T14:22:30Z"]},{"key":"dc:date.issued","label":"Date","values":["2014-02"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology and Woods Hole Oceanographic Institution"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Underwater acoustics","Underwater acoustic telemetry"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.1575/1912/7136"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1912/7136"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2014"]},{"key":"dc:description.abstract","label":"Abstract","values":["Motivated by inspection of complex underwater environments, we have developed a system for multi-sensor SLAM utilizing both structured and unstructured environmental features. We present a system for deriving planar constraints from sonar data, and jointly optimizing the vehicle and plane positions as nodes in a factor graph. We also present a system for outlier rejection and smoothing of 3D sonar data, and for generating loop closure constraints based on the alignment of smoothed submaps. Our factor graph SLAM backend combines loop closure constraints from sonar data with detections of visual fiducial markers from camera imagery, and produces an online estimate of the full vehicle trajectory and landmark positions. We evaluate our technique on an inspection of a decomissioned aircraft carrier, as well as synthetic data and controlled indoor experiments, demonstrating improved trajectory estimates and reduced reprojection error in the final 3D map."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Toward autonomous underwater mapping in partially structured 3D environments"]}]}],"canonical_facts":{"dc:creator":["VanMiddlesworth, Mark"],"dc:date.accessioned":["2015-02-10T14:22:30Z"],"dc:date.available":["2015-02-10T14:22:30Z"],"dc:date.issued":["2014-02"],"dc:description":["Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2014"],"dc:description.abstract":["Motivated by inspection of complex underwater environments, we have developed a system for multi-sensor SLAM utilizing both structured and unstructured environmental features. We present a system for deriving planar constraints from sonar data, and jointly optimizing the vehicle and plane positions as nodes in a factor graph. We also present a system for outlier rejection and smoothing of 3D sonar data, and for generating loop closure constraints based on the alignment of smoothed submaps. Our factor graph SLAM backend combines loop closure constraints from sonar data with detections of visual fiducial markers from camera imagery, and produces an online estimate of the full vehicle trajectory and landmark positions. We evaluate our technique on an inspection of a decomissioned aircraft carrier, as well as synthetic data and controlled indoor experiments, demonstrating improved trajectory estimates and reduced reprojection error in the final 3D map."],"dc:format.mimetype":["application/pdf"],"dc:identifier.doi":["10.1575/1912/7136"],"dc:identifier.uri":["https://hdl.handle.net/1912/7136"],"dc:language.iso":["en_US"],"dc:publisher":["Massachusetts Institute of Technology and Woods Hole Oceanographic Institution"],"dc:subject":["Underwater acoustics","Underwater acoustic telemetry"],"dc:title":["Toward autonomous underwater mapping in partially structured 3D environments"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T22:05:10Z"}