Point Cloud Clustering System with DBSCAN Algorithm for Low-Resolution LiDAR

Sangho Lee, Seongmo An, Raehyeong Kim, Jongwon Oh, Seung Eun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Consumer Electronics, ICCE 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350324136
DOIs
StatePublished - 2024
Event2024 IEEE International Conference on Consumer Electronics, ICCE 2024 - Las Vegas, United States
Duration: 6 Jan 20248 Jan 2024

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
ISSN (Print)0747-668X
ISSN (Electronic)2159-1423

Conference

Conference2024 IEEE International Conference on Consumer Electronics, ICCE 2024
Country/TerritoryUnited States
CityLas Vegas
Period6/01/248/01/24

Keywords

  • DBSCAN
  • FPGA
  • Li-DAR
  • Point Cloud Clustering system

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