Fast euclidean cluster extraction using GPUS

Anh Nguyen, Abraham Monrroy Cano, Masato Edahiro, Shinpei Kato

Research output: Contribution to journalArticle

Abstract

Clustering is the task of dividing an input dataset into groups of objects based on their similarity. This process is frequently required in many applications. How-ever, it is computationally expensive when running on traditional CPUs due to the large number of con-nections and objects the system needs to inspect. In this paper, we investigate the use of NVIDIA graph-ics processing units and their programming platform CUDA in the acceleration of the Euclidean clustering (EC) process in autonomous driving systems. We propose GPU-accelerated algorithms for the EC problem on point cloud datasets, optimization strategies, and discuss implementation issues of each method. Our experiments show that our solution outperforms the CPU algorithm with speedup rates up to 87X on real-world datasets.

Original languageEnglish
Pages (from-to)548-560
Number of pages13
JournalJournal of Robotics and Mechatronics
Volume32
Issue number3
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Autonomous driving systems
  • Euclidean clustering
  • GPGPU
  • Point cloud

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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