@inproceedings{80c00f4258fb4b94bd4eb978b7b0d525,
title = "FPGA-based ORB feature extraction for real-time visual SLAM",
abstract = "Simultaneous Localization And Mapping (SLAM) is the problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. How to enable SLAM robustly and durably on mobile, or even IoT grade devices, is the main challenge faced by the industry today. The main problems we need to address are: 1.) how to accelerate the SLAM pipeline to meet real-time requirements; and 2.) how to reduce SLAM energy consumption to extend battery life. After delving into the problem, we found out that feature extraction is indeed the bottleneck of performance and energy consumption. Hence, in this paper, we design, implement, and evaluate a hardware ORB feature extractor and prove that our design is a great balance between performance and energy consumption compared with ARM Krait and Intel Core i5.",
keywords = "FPGA, Feature extraction, ORB, SLAM",
author = "Weikang Fang and Yanjun Zhang and Bo Yu and Shaoshan Liu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Field-Programmable Technology, ICFPT 2017 ; Conference date: 11-12-2017 Through 13-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/FPT.2017.8280159",
language = "English",
series = "2017 International Conference on Field-Programmable Technology, ICFPT 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "275--278",
booktitle = "2017 International Conference on Field-Programmable Technology, ICFPT 2017",
address = "United States",
}
