Range image-based density-based spatial clustering of application with noise clustering method of three-dimensional point clouds

Mingyun Wen, Seoungjae Cho, Jeongsook Chae, Yunsick Sung, Kyungeun Cho

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Clustering plays an important role in processing light detection and ranging points in the autonomous perception tasks of robots. Clustering usually occurs near the start of processing three-dimensional point clouds obtained from light detection and ranging for detection and classification. Therefore, errors caused by clustering will directly affect the detection and classification accuracy. In this article, a clustering method is presented that combines density-based spatial clustering of application with noise and two-dimensional range image composed by scan lines of light detection and ranging based on the order of generation time. The results show that the proposed method achieves state-of-the-art performance in aspect of time efficiency and clustering accuracy. A ground extraction method based on scan line is also presented in this article, which has strong ability to separate ground points and non-ground points.

Original languageEnglish
JournalInternational Journal of Advanced Robotic Systems
Volume15
Issue number2
DOIs
StatePublished - 1 Mar 2018

Keywords

  • Clustering
  • LiDAR
  • Mobile robot
  • Three-dimensional point cloud
  • Two-dimensional range image

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