{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122157"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122157","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A Bayesian occupancy grid filter for robust pedestrian dead reckoning","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_has_math":false,"creators":["Bhandary Karnoor, Sahil"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Roy Choudhury, Romit"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Bayesian Filtering","Indoor Navigation","Pedestrian Dead Reckoning","Inertial Measurement Units"],"languages":["en","eng"],"rights":["Copyright 2023 Sahil Bhandary Karnoor"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122157","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Roy Choudhury, Romit"]},{"key":"dc:creator","label":"Author","values":["Bhandary Karnoor, Sahil"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-07"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Bayesian Filtering","Indoor Navigation","Pedestrian Dead Reckoning","Inertial Measurement Units"]}]},{"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 Sahil Bhandary Karnoor"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122157"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","The student, Sahil Bhandary Karnoor, accepted the attached license on 2023-12-06 at 14:41.","The student, Sahil Bhandary Karnoor, submitted this Thesis for approval on 2023-12-06 at 19:25.","This Thesis was approved for publication on 2023-12-07 at 15:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20066 on 2024-03-01 at 13:32:07","This thesis considers the problem of indoor localization using inertial sensors (IMUs) that are embedded in almost all smartphones and mobile devices. Significant research has focused on developing pedestrian dead reckoning (PDR) algorithms that utilize the IMU measurements to track human movement. While Particle Filters (PF) have offered the best-known accuracy so far, they are also known to suffer from low robustness. This is not surprising given how IMU data from the real world is highly noisy, mainly due to the arm and limb gestures of the user. Since real-world deployments often favor robustness over accuracy, I propose a Bayesian Occupancy Grid Filter (BOF) that can absorb far greater IMU error compared to PFs. The robustness gains are shown through extensive simulations and the implementation of a fully functional real-time system that performs in accordance with our expectations. BOFs are also simple to implement and can be an important step toward the wide-scale deployment of IMU-based indoor positioning systems."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A Bayesian occupancy grid filter for robust pedestrian dead reckoning"]}]}],"canonical_facts":{"dc:contributor":["Roy Choudhury, Romit"],"dc:creator":["Bhandary Karnoor, Sahil"],"dc:date":["2023-12","2023-12-07"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","The student, Sahil Bhandary Karnoor, accepted the attached license on 2023-12-06 at 14:41.","The student, Sahil Bhandary Karnoor, submitted this Thesis for approval on 2023-12-06 at 19:25.","This Thesis was approved for publication on 2023-12-07 at 15:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20066 on 2024-03-01 at 13:32:07","This thesis considers the problem of indoor localization using inertial sensors (IMUs) that are embedded in almost all smartphones and mobile devices. Significant research has focused on developing pedestrian dead reckoning (PDR) algorithms that utilize the IMU measurements to track human movement. While Particle Filters (PF) have offered the best-known accuracy so far, they are also known to suffer from low robustness. This is not surprising given how IMU data from the real world is highly noisy, mainly due to the arm and limb gestures of the user. Since real-world deployments often favor robustness over accuracy, I propose a Bayesian Occupancy Grid Filter (BOF) that can absorb far greater IMU error compared to PFs. The robustness gains are shown through extensive simulations and the implementation of a fully functional real-time system that performs in accordance with our expectations. BOFs are also simple to implement and can be an important step toward the wide-scale deployment of IMU-based indoor positioning systems."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122157"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Sahil Bhandary Karnoor"],"dc:subject":["Bayesian Filtering","Indoor Navigation","Pedestrian Dead Reckoning","Inertial Measurement Units"],"dc:title":["A Bayesian occupancy grid filter for robust pedestrian dead reckoning"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}