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.
Highlights
With the advancement of three-dimensional (3D) scanning equipment, diverse studies associated with reverse engineering using point cloud data (PCD) are being conducted at present
This study analyzed the limitations of previous studies and generated improvements by presenting a new algorithm for the 3D geometric representation of indoor environments
Given the ±30 mm error caused by the feature of the backpack laser scanners (BLSs) used in this study, the algorithm proposed in this study proves to be suitable for generating the 3D indoor geometric representation
Summary
With the advancement of three-dimensional (3D) scanning equipment, diverse studies associated with reverse engineering using point cloud data (PCD) are being conducted at present. The time-consuming process reduces the efficiency of reverse engineering because an executor who reconstructs the building components (e.g., structural member, non-structural member, furniture) from the raw PCD checks the result of geometric representation iteratively to minimize an error [5,6,7]. Sci. 2020, 10, 8073 since the indoor PCD capture through the porTable 3D scanning method lacks the information for detecting the building components [8,9,10]. To compensate for these limitations, this study proposes a new algorithm capable of generating. The results of this study are expected to be used for the generation of building information modeling (BIM) from PCD that can be used to identify differences in design and construction
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
