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.
Bibliographical noteFunding Information:
Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1A4A1026027).
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- 3D geometric representation
- Automatic algorithm
- Indoor point cloud data
- Normal estimation
- Random sample consensus
ASJC Scopus subject areas
- General Materials Science
- General Engineering
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes