Fast point cloud segmentation based on flood-fill algorithm

Phuong Minh Chu, Seoungjae Cho, Yong Woon Park, Kyungeun Cho

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

18 Scopus citations

Abstract

With the aim of providing a fast and effective segmentation method based on the flood-fill algorithm, in this study, we propose a new approach to segment a 3D point cloud gained by a 3D multi-channel laser range sensor into different objects. First, we divide the point cloud into two groups: ground and nonground points. Next, we segment clusters in each scanline dataset from the group of nonground points. Each scanline cluster is joined with other scanline clusters using the flood-fill algorithm. In this manner, each group of scanline clusters represents an object in the 3D environment. Finally, we obtain each object separately. Experiments show that our method has the ability to segment objects accurately and in real time.

Original languageEnglish
Title of host publicationMFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages656-659
Number of pages4
ISBN (Electronic)9781509060641
DOIs
StatePublished - 7 Dec 2017
Event13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017 - Daegu, Korea, Republic of
Duration: 16 Nov 201718 Nov 2017

Publication series

NameIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Volume2017-November

Conference

Conference13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017
Country/TerritoryKorea, Republic of
CityDaegu
Period16/11/1718/11/17

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