2026-09-21T07:47:31Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/116110 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Chen, Deming 2022-08 application/pdf en text 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 ORB-based SLAM accelerator on SoC FPGA Vemulapati, Vibhakar V 2022-07-18 FPGA Hardware Accelerators SLAM 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. Thesis eng https://hdl.handle.net/2142/116110 Copyright 2022 Vibhakar Vemulapati M.S. Thesis Electrical and Computer Engineering University of Illinois at Urbana-Champaign Electrical and Computer Engineering