## 3 Results  
The verification of the accuracy of the reference cloud (Leica P40) using 21 control points independently georeferenced using the Trimble S9 HP total station proved high accuracy of that cloud, with an _RMSD_ of 0.005 m. In view of the manufacturer-declared SLAM scanner accuracy (0.01-0.03 m), the reference cloud accuracy was sufficient. The reference point cloud was also diluted to a resolution of 3 mm (the distance of neighboring points of 3 mm or less) using the Subsample, a point cloud function in Cloud Compare. The diluted cloud contained 39,078,802 points. The GeoSLAM 1 and GeoSLAM 2 clouds were not diluted, containing 19,605,824 and 8,170,880 points, respectively. The number of GCPs for calculations of the transformation parameters is shown in Table 1.  
Figure 10 shows a cross-section of one of the pillars, enabling a visual comparison of the Leica P40 reference cloud, GeoSLAM 1 cloud, and the denoised GeoSLAM 1-MLS cloud. The differences in quality are obvious, especially for the un-smoothened GeoSLAM cloud suffering from substantially greater noise, the magnitude of which, moreover, differed throughout the object (which was probably associated with the distance between the scanner and the object at the moment of scanning of the respective part). GeoSLAM MLS data were much smoother; however, rounding of corners could be noted (resulting both from the uncertainty in the input data and from the surface approximation). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data** & **Number of Points** & **Subsampling [m]** & **Number of GCPs** & **RMSE [m]** & **Acquisition Time** & **Processing Time** \\ \hline Leica P40 & 39,078,802 & 0.003 & 7 & 0.003 & 3 h & 30 min \\ GeoSLAM 1 & 19,605,824 & - & 7 & 0.059 & 30 min & 45 min \\ GeoSLAM 2 & 8,170,880 & - & 7 & 0.064 & 30 min & 45 min \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of point clouds and results of point cloud transformation to the Czech coordinate system. Figure 9: Flowchart summarizing the data measurement and processing procedure. Next, we evaluated the overall agreement between the GeoSLAM and reference clouds. The results (_RMSDs_) are shown in Table 2. These global characteristics showed a very good agreement of the GeoSLAM clouds with the reference cloud. Denoising by MLS improved the accuracy in all comparisons, although not to a major degree, which was understandable in view of the magnitude of the noise (see also Figures 11 and 12). In general, the high noise of GeoSLAM Zeb Horizon clouds \"hides\" the possible systematic differences (Figure 10). MLS-smoothed data were much clearer. For this reason, only MLS-smoothed data will be discussed further as they could better reveal the systematic distortions of the clouds. Figures 11 and 12 visualize the comparison to the reference cloud of MLS data and for MLS/ICP data. Both passes with the GeoSLAM Zeb Horizon scanner showed the same pattern, with better accuracies at the beginning and end of the bridge, while in the middle of the bridge, the accuracy was lower and _RMSDs_ reached up to 0.04 m. For ICP-transformed clouds (bottom panels of Figures 11 and 12), the _RMSDs_ were generally lower, but the shape (and/or size) of the GeoSLAM cloud differed from that of the reference one. _RMSDs_ were bigger at the beginning and end of the bridge (up to 0.05) and smaller in the middle \begin{table}
\begin{tabular}{c c c c c} \hline  & **Original** & **Denoised (MLS)** & **Original Data** & **Denoised (MLS) Data** \\
**Data** & **Cloud** & **Cloud** & **ICP** & **ICP** \\  & **RMSD [m]** & **RMSD [m]** & **RMSD [m]** & **RMSD [m]** \\ \hline GeoSLAM 1 & 0.021 & 0.017 & 0.015 & 0.010 \\ GeoSLAM 2 & 0.025 & 0.023 & 0.019 & 0.015 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of whole GeoSLAM and P40 clouds. Figure 10: Visualization of the pillar point cloud—top view—acquired by (**a**) P40, (**b**) GeoSLAM, and (**c**) GeoSLAM-MLS, and the same in izometric view: (**d**) P40, (**e**) GeoSLAM, and (**f**) GeoSLAM-MLS.