Normal Grouping Density Separation (NGDS): A Novel Object-Driven Indoor Point Cloud Partition Method

Walczak, Jakub; Andrzejczak, Grzegorz; Scherer, Rafał; Wojciechowski, Adam

doi:10.1007/978-3-030-50433-5_8

Cited by 1 publication

(1 citation statement)

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“…Since the adjacent rooms share an inner wall whose thickness is thinner than the vertical structural member, the existing method uses the LiDAR-oriented normal vector to minimize the error. The normal vector-based segmentation divides the under-segmented inner wall into two clusters that are included in different rooms respectively [26].…”

Section: Introductionmentioning

confidence: 99%

Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing

Lee

Kim

et al. 2021

Remote Sensing

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With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS.

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Section: Introductionmentioning

confidence: 99%