# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 3 Experiment Design and Analysis Method
### Data Acquisition  
MLS acquires point cloud data using a SLAM-based approach that accounts for scanner movement. The data acquisition method of MLS generally includes a closed-loop algorithm, which reduces accumulated positional errors by performing feature matching between the start and end points of the scan and correcting the overall trajectory. Therefore, handheld MLS devices used in this study have difficulty obtaining stable point cloud data from a fixed position, unlike TLS. Therefore, the present experiment adopted a data acquisition approach where MLS point clouds were collected by traveling along a predefined path within a laboratory space to ensure the stable acquisition of handheld MLS data.  
To ensure precise analysis, several constraints were applied during the acquisition of MLS point clouds. The MLS operator followed a predefined scanning path while capturing the MLS point clouds. Figure 2 illustrates the top view of the experimental room, depicting the installation of Column A and Column B, the MLS scanning path, and the scan positions of the TLS for reference data acquisition.  
The MLS scanning path was designed to maintain a two-meter distance between the Columns and the MLSs. A temporary wall was installed to prevent data acquisition from excessive distances. These constraints were implemented to reduce the impact of distance-induced errors. The TLS scan positions were set at six locations to ensure comprehensive data acquisition for all surfaces of the Columns. The TLS point clouds obtained from these six positions were precisely registered using commercial software associated with TLS device and utilized as reference data. The registration error between the six TLS point clouds was measured at 0.5 mm. This 0.5 mm indicates that the average distance between corresponding point pairs during the registration of six point clouds was measured to be 0.5 mm. Such registration errors are inevitable in point cloud registration, particularly due to variations in point density and the characteristics of space-level data registration. Therefore, the 0.5 mm value is considered sufficiently reliable for use as reference data.  
Figure 1: Design of Column A and Column B.  
The acquisition of MLS point clouds was performed four times for each MLS device (MLS-01, MLS-02, and MLS-03). These four scans enable verification of whether the MLS point clouds consistently exhibit the same error distribution pattern across multiple scans. However, one dataset from MLS-01 was missing, so only three datasets were used in the actual analysis. Further details are provided in Section 4.1. Two scans were initiated from the left-hand scanning point as shown in Figure 2, while the other two started from the right scanning start position. This approach considers the possibility that the error distribution pattern may vary depending on the specific moment when the analysis target is captured during MLS scanning. Figure 3 presents a photograph of the experimental room where the actual Columns were installed.  
The MLS point clouds were processed using the software provided with each MLS device. As a result, procedures such as SLAM algorithms and loop closing techniques, which are built into the device and software, are applied automatically to generate complete MLS point clouds. However, users typically cannot access the detailed implementation of these proprietary algorithms. For this reason, in our study, we applied only the minimum procedures necessary to produce complete point clouds and did not include any additional noise filtering or post-processing specific to each software.  
Figure 3: Installed Column A and Column B in the experiment room.  
Figure 2: Top view of the experimental room and MLS/TLS scanning plan.