{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127459"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127459","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Perception and mapping for unmanned ground vehicles in unstructured environments","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","abstract_has_math":false,"creators":["Cheng, Weihao"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Norris, William R","Hovakimyan, Naira"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-11-20","date_published":"2024-11-20","updated_at":"2026-07-22T22:25:04Z","subjects":["Autonomous Vehicles","Perception","Negative Obstacle Detection","Sensor Fusion","Lidar"],"languages":["en","eng"],"rights":["Copyright 2024 Weihao Cheng"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127459","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Norris, William R","Hovakimyan, Naira"]},{"key":"dc:creator","label":"Author","values":["Cheng, Weihao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-11-20","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Autonomous Vehicles","Perception","Negative Obstacle Detection","Sensor Fusion","Lidar"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Weihao Cheng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127459"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","The student, Weihao Cheng, accepted the attached license on 2024-11-19 at 09:59.","The student, Weihao Cheng, submitted this Thesis for approval on 2024-11-19 at 10:34.","This Thesis was approved for publication on 2024-11-20 at 14:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21318 on 2025-03-28 at 14:54:31","This research work presents a comprehensive study on enhancing the perception and mapping capabilities of Unmanned Ground Vehicles (UGVs) operating in unstructured environments. The key perception challenges addressed include effective ground segmentation, negative obstacle detection, and terrain traversability mapping, all crucial for enabling safe UGV navigation in complex terrains. First, this research work explores and implements state-of-the-art ground segmentation methods. Then, this work conducts theoretical analyses on LiDAR-based negative obstacle detection and proposes a novel detection and mapping method. Additionally, a multi-modal terrain traversability mapping method is explored and implemented in this work. This multi-modal method integrates elevation data and visual information for more accurate terrain traversability mapping. The proposed techniques are tested on several autonomous platforms, demonstrating potential improvements in perception and mapping. Overall, the preliminary results are promising and provide great insights into future research to enhance UGV’s perception capabilities in hazardous environments."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Perception and mapping for unmanned ground vehicles in unstructured environments"]}]}],"canonical_facts":{"dc:contributor":["Norris, William R","Hovakimyan, Naira"],"dc:creator":["Cheng, Weihao"],"dc:date":["2024-11-20","2024-12"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","The student, Weihao Cheng, accepted the attached license on 2024-11-19 at 09:59.","The student, Weihao Cheng, submitted this Thesis for approval on 2024-11-19 at 10:34.","This Thesis was approved for publication on 2024-11-20 at 14:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21318 on 2025-03-28 at 14:54:31","This research work presents a comprehensive study on enhancing the perception and mapping capabilities of Unmanned Ground Vehicles (UGVs) operating in unstructured environments. The key perception challenges addressed include effective ground segmentation, negative obstacle detection, and terrain traversability mapping, all crucial for enabling safe UGV navigation in complex terrains. First, this research work explores and implements state-of-the-art ground segmentation methods. Then, this work conducts theoretical analyses on LiDAR-based negative obstacle detection and proposes a novel detection and mapping method. Additionally, a multi-modal terrain traversability mapping method is explored and implemented in this work. This multi-modal method integrates elevation data and visual information for more accurate terrain traversability mapping. The proposed techniques are tested on several autonomous platforms, demonstrating potential improvements in perception and mapping. Overall, the preliminary results are promising and provide great insights into future research to enhance UGV’s perception capabilities in hazardous environments."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127459"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Weihao Cheng"],"dc:subject":["Autonomous Vehicles","Perception","Negative Obstacle Detection","Sensor Fusion","Lidar"],"dc:title":["Perception and mapping for unmanned ground vehicles in unstructured environments"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}