# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 2 Literature Review  
### Analysis of MLS Point Clouds  
Most of the related studies analyzed MLS devices based on comparison between MLS point clouds and reference data. [PERSON] et al. [19] evaluated the performance of vehicle-mounted MLSs using precisely measured feature points, applying metrics of absolute errors and standard deviations to assess performance. [PERSON] et al. [20] assessed the applicability of MLSs for architectural components like doors and windows, using manually measured dimensions as reference data and comparing them with dimensions extracted from point clouds. [PERSON] et al. [21] evaluated the performance of vehicle-mounted MLSs by calculating the area of targets, such as windows on walls. The area was then compared to the outline of the target. The outline was generated by applying the alpha-shape algorithm to the point clouds obtained from MLSs. These studies used precisely measured feature points or areas of targets for comparative analysis.  
Several studies have used TLS data as reference data. The ICP registration algorithm is commonly employed to align MLS point clouds (comparison data) with TLS point clouds (reference data). In this process, the MLS point clouds were translated and rotated to minimize the root mean square error (RMSE) between them and the TLS point clouds. [PERSON] et al. [22] evaluated the accuracy of the RIEGL VMX-450 by aligning photogrammetry and TLS-based reference point clouds using ICP and analyzing the error distribution with non-parametric statistics to account for non-Gaussian characteristics. [PERSON] et al. [23] segmented MLS point clouds into temporal slices and applied planar feature-based registration to TLS data. [PERSON] et al. [24] conducted object-based comparisons between MLS and TLS, including Pegasus Two and Backpack systems, using TLS point clouds as references. [PERSON] et al. [25] assessed discrepancies between MLS point clouds and TLS references using both multi model-to-model cloud comparison (M3C2) and cloud-to-mesh (C2M) distances. [PERSON] et al. [26] compared robotic and handheld MLS modes against TLS benchmarks by analyzing point distribution and repeatability in GNSS-denied environments. [PERSON] et al. [27] quantified discrepancies between the BLK360 and Riegl VZ400i at both object and structural levels using TLS data as the baseline. Subsequently, deviations between the aligned TLS and MLS point clouds were either measured using cloud-to-cloud (C2C) distances [23; 24; 26; 27], or analyzed based on semantic features of the objects [22]. Additionally, [PERSON] et al. [25] performed a comparative analysis of feature points following ICP registration. Furthermore, TLS point clouds, total station measurements [8; 28], and synthetic reference models derived from as-planned BIMs [29] were also used for evaluating MLS performance. [PERSON] and [PERSON] [30] used MLS point clouds as references to evaluate the feasibility of implementing a scan-to-BIM workflow using an iPad LiDAR sensor. They extracted planar elements from MLS point clouds and calculated C2M distances and comparing them with measurements obtained from manually created as-built BIMs.  
To summarize, previous studies have performed analysis of MLS point clouds using TLS point clouds, precisely measured feature points, target areas, or benchmark point clouds generated from as-planned BIMs. These studies analyze MLS point clouds using distance error-based metrics which are measured using C2C or C2M distances. However, previous studies present challenges to understanding the error distribution pattern in MLS point clouds. In addition, previous analyses using ICP registration are often applied to either a single surface of the analysis target or the global point clouds. Consequently, the unique error distribution patterns of MLS point clouds may be overlooked. Therefore, it is necessary to develop a method that analyzes the error distribution pattern in MLS point clouds.