@inproceedings{8db4efb938fb44ee964968c38a702289,
title = "NORB: A Stream-Based and Non-Blocking FPGA Accelerator for ORB Feature Extraction",
abstract = "Oriented FAST and Rotating BRIEF (ORB) is a key technique for visual feature extraction and matching, which forms the foundation of the state-of-the-art ORB-SLAM systems. Due to the computational complexity, FPGA accelerators for ORB computing are usually used when running ORB-SLAM on low-power platforms. Previous implementations of ORB accelerators need to block the input stream when computing the rBRIEF descriptors, and cannot achieve high throughput on all pixels. In this paper, we propose a stream-based accelerator of ORB feature extraction which achieves non-blocking computation by caching the columns of the corresponding window buffer. The proposed accelerator is implemented on a Zynq UltraScale SoC, and the experimental result shows it achieves an average latency of 1.4ms, which is 44% faster than the state-of-the-art approach, with similar output accuracy. The system has a low resource utilization with power consumption being only 1.5W.",
keywords = "Feature Extraction, FPGA Accelerator, ORB, ORB-SLAM",
author = "Qixing Zhang and Hao Sun and Qi Deng and Heng Yu and Yajun Ha",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023 ; Conference date: 04-12-2023 Through 07-12-2023",
year = "2023",
doi = "10.1109/ICECS58634.2023.10382726",
language = "English",
series = "ICECS 2023 - 2023 30th IEEE International Conference on Electronics, Circuits and Systems: Technosapiens for Saving Humanity",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICECS 2023 - 2023 30th IEEE International Conference on Electronics, Circuits and Systems",
address = "United States",
}