Algorithm for generating 3d geometric representation based on indoor point cloud data

Min Woo Ryu, Sang Min Oh, Min Ju Kim, Hun Hee Cho, Chang Baek Son, Tae Hoon Kim

Research output: Contribution to journalArticlepeer-review

Abstract

This study proposes a new method to generate a three-dimensional (3D) geometric representation of an indoor environment by refining and processing an indoor point cloud data (PCD) captured through backpack laser scanners. The proposed algorithm comprises two parts to generate the 3D geometric representation: data refinement and data processing. In the refinement section, the inputted indoor PCD are roughly segmented by applying random sample consensus (RANSAC) to raw data based on an estimated normal vector. Next, the 3D geometric representation is generated by calculating and separating tangent points on segmented PCD. This study proposes a robust algorithm that utilizes the topological feature of the indoor PCD created by a hierarchical data process. The algorithm minimizes the size and the uncertainty of raw PCD caused by the absence of a global navigation satellite system and equipment errors. The result of this study shows that the indoor environment can be converted into 3D geometric representation by applying the proposed algorithm to the indoor PCD.

Original languageEnglish
Article number8073
Pages (from-to)1-13
Number of pages13
JournalApplied Sciences (Switzerland)
Volume10
Issue number22
DOIs
Publication statusPublished - 2020 Nov 2

Keywords

  • 3D geometric representation
  • Automatic algorithm
  • Indoor point cloud data
  • Normal estimation
  • Random sample consensus

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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