NDLI: Algorithm for Generating 3D Geometric Representation Based on Indoor Point Cloud Data

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Algorithm for Generating 3D Geometric Representation Based on Indoor Point Cloud Data

Content Provider	MDPI
Author	Ryu, Min Woo Oh, Sang Min Kim, Min Ju Cho, Hun Hee Son, Chang Baek Kim, Tae Hoon
Copyright Year	2020
Description	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.
Starting Page	8073
e-ISSN	20763417
DOI	10.3390/app10228073
Journal	Applied Sciences
Issue Number	22
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2020-11-14
Access Restriction	Open
Subject Keyword	Applied Sciences Marine Engineering Remote Sensing Indoor Point Cloud Data 3d Geometric Representation Automatic Algorithm Random Sample Consensus Normal Estimation
Content Type	Text
Resource Type	Article

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