# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 4 Results
### Verification of Dimensional Correction Using Error Distribution Patterns  
The goal of this study is to identify the error distribution patterns of MLS point clouds and enhance the dimensional accuracy of as-built BIMs generated from MLS data. In this section, we verify whether the representative mean error derived for each MLS device improves the dimensional accuracy of 3D modeling for actual building components.  
For verification, the same method used to generate reference planes in previous experiments was applied to the MLS point clouds. RANSAC was employed to extract planes, and fixed z-values were assigned to the intersection lines of these planes to obtain eight vertices (MLS-RANSAC). The eight derived vertices were compared with the reference vertices obtained from TLS point clouds using RANSAC, and the errors were measured. The average distance measured at these eight vertices was defined as the error value of the column.  
The representative mean error of each MLS device was used to translate the RANSAC planes accordingly. From the corrected plane equations, intersection lines were obtained, and fixed z-values were assigned to derive eight corrected vertices. Finally, the verification was conducted by comparing the errors of the eight vertices derived from MLS-RANSAC with those obtained using MLS-RANSAC with correction.  
The verification results demonstrate that applying the representative mean error derived in this study significantly improved the dimensional accuracy of 3D modeling. Table 5 presents the errors measured for all datasets, while Figure 14 shows the error reduction for each MLS device. Except for Column A in Dataset 03 of MLS-03, error reduction was observed in all Columns. According to Figure 14, the error reduction for MLS-01, MLS-02, and MLS-03 was 2.7 mm, 9.7 mm, and 1.3 mm, respectively. Notably, MLS-02 exhibited the most significant improvement. This result suggests that although MLS-02 had larger error values, its error distribution pattern was more consistent than that of MLS-01, leading to a more effective correction. These findings indicate that even if an MLS device exhibits substantial errors, a consistent and well-defined error distribution pattern enables accurate dimensional correction of building components.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**MLS Type**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Column**} & \multicolumn{2}{c}{**MLS-**} & \multirow{2}{*}{**RANSAC**} & \multirow{2}{*}{**Accuracy**} \\  & & & **RANSAC** & & & \\  & & & **(mm)** & & & \\ \hline \multirow{8}{*}{MLS-01} & \multirow{4}{*}{02} & A & 10.7 & 5.0 & 5.7 \\  & & B & 7.0 & 4.1 & 2.9 \\ \cline{1-1} \cline{2-6}  & & A & 5.8 & 5.2 & 0.6 \\  & & B & 5.5 & 4.8 & 0.8 \\ \cline{1-1} \cline{2-6}  & & A & 9.0 & 4.5 & 4.5 \\  & & B & 4.7 & 2.9 & 1.8 \\ \hline \multirow{8}{*}{MLS-02} & \multirow{4}{*}{01} & A & 11.8 & 5.8 & 6.0 \\  & & B & 12.8 & 2.7 & 10.1 \\ \cline{1-1} \cline{2-6}  & & A & 13.4 & 4.9 & 8.5 \\ \cline{1-1}  & & B & 12.3 & 2.9 & 9.4 \\ \cline{1-1} \cline{2-6}  & & A & 13.3 & 2.3 & 11.0 \\ \cline{1-1} \cline{2-6}  & & B & 12.1 & 1.9 & 10.2 \\ \cline{1-1} \cline{2-6}  & & A & 14.3 & 3.0 & 11.3 \\ \cline{1-1} \cline{2-6}  & & B & 13.2 & 2.3 & 10.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Error measurement results of MLS-RANSAC and MLS-RANSAC with correction methods.  
Despite employing a general approach, this verification demonstrated a remarkable improvement in dimensional accuracy. Therefore, if more precise methods for error distribution measurement and correction are applied in the future, this approach will significantly contribute to inspection processes and the high-precision generation of as-built BIMs using MLS point clouds.