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.

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

“…A specific subtype of the MLS is a Backpack Laser Scanner (BLS), a version in which a human operator carries the device as a backpack. Such a solution was found in the publication of Ryu et al [57]. They successfully used indoor point cloud data gathered by a BLS to generate a geometric representation of found rooms, utilizing a two-step procedure based on the data refinement and later processing.…”

Normal Classification of 3d Occupancy Grids for Voxel-Based Indoor Reconstruction From Point Clouds

“…Automated reconstruction of building environments from indoor mapping data such as point clouds is a wide and active field of research (Kang et al, 2020;Pintore et al, 2020). The various proposed approaches differ significantly in the amount of assumptions that are made with respect to the building structure to be reconstructed and thus in their flexibility towards challenging building environments, ranging from single room scenarios (Li et al, 2020;Sanchez et al, 2020b), Manhattan World structures where all surfaces are orthogonal to the coordinate axes (Ryu et al, 2020;Kim et al, 2020) to diagonal (Shi et al, 2019;Tran and Khoshelham, 2020) or even curved walls (Yang et al, 2019;Wu et al, 2020) and slanted ceilings (Nikoohemat et al, 2020;Lim and Doh, 2021).…”

Increased plane identification precision with stereo identification

“…Construction robots often encounter plane identification problems, such as those found in wall painting robots [21], floor surface profiling robots [22], ground plane detection [23][24][25], and surface reconstruction [26,27]. Methods for plane extraction and parameter identification include random sample consensus (RANSAC) and its variants.…”