{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/136908"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/136908","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Autonomous Vehicle Pose Estimation in GNSS-Denied Areas Using Cross-Track Error Measurements","abstract":"Accurate pose estimation is critical for the safe and efficient operation of autonomous vehicles, enabling precise navigation and control in highly dynamic environments. This study presents a novel approach to vehicle pose estimation using cross-track error (CTE) measurements as supplemental inputs for GNSS denied environments. The proposed method leverages CTE, a measure of lateral deviation from a desired trajectory, to estimate the vehicle’s position and orientation in real-time using an extended Kalman filter (EKF) framework. By integrating CTE into an existing pose estimation architecture, the new system is able to provide vehicle state estimates with a greater accuracy than conventional inertial navigation systems (INS). Simulation studies were conducted on benchmark test sets, demonstrating the method’s effectiveness at reducing pose estimation errors by up to 98% in various GNSS denied situations. This research contributes to the advancement of robust and cost-efficient localization strategies, paving the way for safer autonomous vehicle navigation.","abstract_html":"Accurate pose estimation is critical for the safe and efficient operation of autonomous vehicles, enabling precise navigation and control in highly dynamic environments. This study presents a novel approach to vehicle pose estimation using cross-track error (CTE) measurements as supplemental inputs for GNSS denied environments. The proposed method leverages CTE, a measure of lateral deviation from a desired trajectory, to estimate the vehicle’s position and orientation in real-time using an extended Kalman filter (EKF) framework. By integrating CTE into an existing pose estimation architecture, the new system is able to provide vehicle state estimates with a greater accuracy than conventional inertial navigation systems (INS). Simulation studies were conducted on benchmark test sets, demonstrating the method’s effectiveness at reducing pose estimation errors by up to 98% in various GNSS denied situations. This research contributes to the advancement of robust and cost-efficient localization strategies, paving the way for safer autonomous vehicle navigation.","abstract_has_math":false,"creators":["Moomaw, Andrew Jacob"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Southward, Steve C."],"committee_members":["Wicks, Alfred L.","Abbott, A. 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The results of this testing are promising as they indicate a significant improvements over existing pose estimation solutions. This research contributes this new method of CTE based pose estimation to the field of vehicle pose estimation, in hopes that the autonomous vehicle environment will become safer and more accessible to all."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Autonomous Vehicle Pose Estimation in GNSS-Denied Areas Using Cross-Track Error Measurements"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Southward, Steve C."],"dc:contributor.committeemember":["Wicks, Alfred L.","Abbott, A. 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