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

doi:10.3390/app10228073

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

School of Civil, Environmental and Architectural Engineering

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

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 language	English
Article number	8073
Pages (from-to)	1-13
Number of pages	13
Journal	Applied Sciences (Switzerland)
Volume	10
Issue number	22
DOIs	https://doi.org/10.3390/app10228073
Publication status	Published - 2020 Nov 2

Bibliographical note

Funding Information:
Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1A4A1026027).

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

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

ASJC Scopus subject areas

General Materials Science
Instrumentation
General Engineering
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes

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10.3390/app10228073

Cite this

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title = "Algorithm for generating 3d geometric representation based on indoor point cloud data",

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.",

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author = "Ryu, {Min Woo} and Oh, {Sang Min} and Kim, {Min Ju} and Cho, {Hun Hee} and Son, {Chang Baek} and Kim, {Tae Hoon}",

note = "Funding Information: Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1A4A1026027). Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",

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AU - Kim, Min Ju

AU - Cho, Hun Hee

AU - Son, Chang Baek

AU - Kim, Tae Hoon

N1 - Funding Information: Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1A4A1026027). Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2020/11/2

Y1 - 2020/11/2

N2 - 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.

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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