{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116110"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116110","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"ORB-based SLAM accelerator on SoC FPGA","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","abstract_has_math":false,"creators":["Vemulapati, Vibhakar V"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Chen, Deming"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["FPGA","Hardware Accelerators","SLAM"],"languages":["en","eng"],"rights":["Copyright 2022 Vibhakar Vemulapati"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116110","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chen, Deming"]},{"key":"dc:creator","label":"Author","values":["Vemulapati, Vibhakar V"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-18"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer 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":["FPGA","Hardware Accelerators","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 2022 Vibhakar Vemulapati"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116110"]}]},{"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 2024-08-01","The student, Vibhakar Vemulapati, accepted the attached license on 2022-07-15 at 02:29.","The student, Vibhakar Vemulapati, submitted this Thesis for approval on 2022-07-15 at 02:37.","This Thesis was approved for publication on 2022-07-18 at 13:40.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18329 on 2022-11-15 at 21:40:28","Simultaneous Localization and Mapping (SLAM) is one of the main components of autonomous navigation systems. With the increase in popularity of drones, autonomous navigation on low-power systems is seeing widespread application. Most SLAM algorithms are computationally intensive and struggle to run in real-time on embedded devices with reasonable accuracy. ORB-SLAM is an open-sourced feature-based SLAM that achieves high accuracy with reduced computational complexity. We propose an SoC based ORB-SLAM system that accelerates the computationally intensive visual feature extraction and matching on hardware. Our FPGA system based on a Zynq-family SoC runs 8.5x, 1.55x and 1.35x faster compared to an ARM CPU, Intel Desktop CPU, and a state-of-the-art FPGA system respectively, while averaging a 2x improvement in accuracy compared to prior work on FPGA."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["ORB-based SLAM accelerator on SoC FPGA"]}]}],"canonical_facts":{"dc:contributor":["Chen, Deming"],"dc:creator":["Vemulapati, Vibhakar V"],"dc:date":["2022-08","2022-07-18"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","The student, Vibhakar Vemulapati, accepted the attached license on 2022-07-15 at 02:29.","The student, Vibhakar Vemulapati, submitted this Thesis for approval on 2022-07-15 at 02:37.","This Thesis was approved for publication on 2022-07-18 at 13:40.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18329 on 2022-11-15 at 21:40:28","Simultaneous Localization and Mapping (SLAM) is one of the main components of autonomous navigation systems. With the increase in popularity of drones, autonomous navigation on low-power systems is seeing widespread application. Most SLAM algorithms are computationally intensive and struggle to run in real-time on embedded devices with reasonable accuracy. ORB-SLAM is an open-sourced feature-based SLAM that achieves high accuracy with reduced computational complexity. We propose an SoC based ORB-SLAM system that accelerates the computationally intensive visual feature extraction and matching on hardware. Our FPGA system based on a Zynq-family SoC runs 8.5x, 1.55x and 1.35x faster compared to an ARM CPU, Intel Desktop CPU, and a state-of-the-art FPGA system respectively, while averaging a 2x improvement in accuracy compared to prior work on FPGA."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116110"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Vibhakar Vemulapati"],"dc:subject":["FPGA","Hardware Accelerators","SLAM"],"dc:title":["ORB-based SLAM accelerator on SoC FPGA"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}