{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121406"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121406","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Introspective learning based Visual-LiDAR fusion for adaptive Simultaneous Localization and Mapping","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","abstract_has_math":false,"creators":["Kedia, Shubham"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Hauser, Kris"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08","date_published":"2023-08","updated_at":"2026-07-22T22:24:57Z","subjects":["Slam","Computer Vision","Sensor Fusion","State Estimation","Deep Learning","Robotics","Optimization","Adaptive Slam"],"languages":["en","eng"],"rights":["Copyright 2023 Shubham Kedia"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/121406","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hauser, Kris"]},{"key":"dc:creator","label":"Author","values":["Kedia, Shubham"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-08","2023-06-08"]},{"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":["Slam","Computer Vision","Sensor Fusion","State Estimation","Deep Learning","Robotics","Optimization","Adaptive Slam"]}]},{"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 2023 Shubham Kedia"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/121406"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Shubham Kedia, accepted the attached license on 2023-05-30 at 16:16.","The student, Shubham Kedia, submitted this Thesis for approval on 2023-05-30 at 16:17.","This Thesis was approved for publication on 2023-06-08 at 09:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19398 on 2023-12-04 at 16:59:49","This work is about developing an adaptive Visual-LiDAR Simultaneous Localization and Mapping (SLAM) algorithm. The objective is to develop a SLAM system that can adaptively negotiate LiDAR degenerate scenarios and visually challenging environments using sensor fusion. The fusion is based on the Pose Graph Optimization (PGO) technique, utilizing adaptive fusion weights predicted from a Deep Neural Network (DNN) model. The DNN model framework is inspired by introspective learning for vision systems. The DNN model is trained on a large dataset called TartanAir, which has diverse and challenging environmental conditions. The output of the model is the predicted error on the visual odometry and LiDAR odometry, which is used to compose the information matrix of the PGO. The PGO framework with weighted pose constraints from visual odometry, LiDAR odometry, and loop closure is solved using the Levenberg–Marquardt optimization algorithm. The proposed framework shows superior performance compared to the visual-only, LiDAR-only SLAM, and baseline fusion methods that were evaluated in this study."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Introspective learning based Visual-LiDAR fusion for adaptive Simultaneous Localization and Mapping"]}]}],"canonical_facts":{"dc:contributor":["Hauser, Kris"],"dc:creator":["Kedia, Shubham"],"dc:date":["2023-08","2023-06-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Shubham Kedia, accepted the attached license on 2023-05-30 at 16:16.","The student, Shubham Kedia, submitted this Thesis for approval on 2023-05-30 at 16:17.","This Thesis was approved for publication on 2023-06-08 at 09:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19398 on 2023-12-04 at 16:59:49","This work is about developing an adaptive Visual-LiDAR Simultaneous Localization and Mapping (SLAM) algorithm. The objective is to develop a SLAM system that can adaptively negotiate LiDAR degenerate scenarios and visually challenging environments using sensor fusion. The fusion is based on the Pose Graph Optimization (PGO) technique, utilizing adaptive fusion weights predicted from a Deep Neural Network (DNN) model. The DNN model framework is inspired by introspective learning for vision systems. The DNN model is trained on a large dataset called TartanAir, which has diverse and challenging environmental conditions. The output of the model is the predicted error on the visual odometry and LiDAR odometry, which is used to compose the information matrix of the PGO. The PGO framework with weighted pose constraints from visual odometry, LiDAR odometry, and loop closure is solved using the Levenberg–Marquardt optimization algorithm. The proposed framework shows superior performance compared to the visual-only, LiDAR-only SLAM, and baseline fusion methods that were evaluated in this study."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121406"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Shubham Kedia"],"dc:subject":["Slam","Computer Vision","Sensor Fusion","State Estimation","Deep Learning","Robotics","Optimization","Adaptive Slam"],"dc:title":["Introspective learning based Visual-LiDAR fusion for adaptive Simultaneous Localization and Mapping"],"dc:type":["text"],"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":"2026-07-22T22:24:57Z"}