@inproceedings{3b94486f95d1442a950ca5e7408da390,
title = "Point Cloud Clustering System with DBSCAN Algorithm for Low-Resolution LiDAR",
abstract = "LiDAR point cloud clustering is a crucial part of object detection and recognition. However, clustering enormous point cloud of LiDAR assigns a large processing load to an on-board device in a vehicle. In this paper, we propose point cloud clustering system with a density-based spatial clustering of applications with noise (DBSCAN) algorithm for low-resolution LiDAR, offloading clustering tasks and shortening the processing time. In order to verify the feasibility of the system, we implemented the point cloud clustering accelerator on a field programmable gate array (FPGA). The system demonstrated 39.5 times enhancement in the processing speed.",
keywords = "DBSCAN, FPGA, Li-DAR, Point Cloud Clustering system",
author = "Sangho Lee and Seongmo An and Raehyeong Kim and Jongwon Oh and Lee, \{Seung Eun\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Consumer Electronics, ICCE 2024 ; Conference date: 06-01-2024 Through 08-01-2024",
year = "2024",
doi = "10.1109/ICCE59016.2024.10444271",
language = "English",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE International Conference on Consumer Electronics, ICCE 2024",
}