# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 3 Results
### Visual Comparison of the Clouds  
To illustrate the behavior of both the systematic and random error components, visual comparisons were prepared. The visualizations of SLAM scanner clouds were always based on the second passage. The figures always show the point clouds (a) transformed to spherical targets (their positions are shown in the figures) and (b) transformed using ICP. The respective RMSE values [m] are always shown in the upper part of the panel. Note that due to the differences in the maximum error values, the color coding of the errors (shown in the vertical bar next to each figure) could not be kept at an identical scale throughout all images.  
The visualization of the Trimble X7 scanner clouds (Figure 9) shows a predominance of systematic (bending) error in this scanner. In the Figure 9a, showing the cloud transformed to spherical targets, the errors at the extreme ends are low due to the \"pinning\" of the cloud to the spherical targets, while the highest error can be observed in the middle. The Figure 9b presenting the result of ICP transformation shows an increase in the errors at the ends and a drop in the central part compared to the cloud (a). The maximum observed error was 2 cm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scanner** & **Trimble X7** & **Leica RTC 360** & \begin{tabular}{c} **Emesent** \\ **Hovermap ST-X** \\ \end{tabular} & \
\begin{tabular}{c} **GeoSLAM ZEB** \\ **Horizon RT** \\ \end{tabular} & **FARO Orbis** \\ \hline
\begin{tabular}{c} \(\text{RMSE}_{\text{SPHER}}\) [mm] for \\ individual \\ measurements \\ \(\text{RMSE}_{\text{SPHER},\text{E}}\) [mm] \\ (StDev) \\ \end{tabular} & 2.5 & 1.0 & 5.6/5.4/6.5 & 20.6/21.9/27.8 & 6.3/6.0/5.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relative accuracies of individual clouds (after ICP transformation) and standard deviation (StDev in the brackets).  
Figure 9: The visualization of the errors of the cloud acquired using Trimble X7 (**a**) after transformation to spherical targets (\(\text{RMSE}_{\text{OVERALL}}=5.4\) mm); and (**b**) after ICP transformation (\(\text{RMSE}_{\text{ICP-ALL}}=2.5\) mm).  
In the point cloud acquired using Leica RTC 360 (Figure 10), the color-coding appears to indicate, at first sight, the presence of the systematic (shape) error. However, the fact that RMSE dropped only slightly after ICP transformation indicates the predominance of the random error. The maximum error is only 7 mm in this point cloud, which is the best value of all tested scanners.  
In the point cloud acquired using the Einesent Hovermap ST-X (Figure 11), the random error predominates (likely due to the higher noise level). The maximum error is 3 cm.  
The RMSE of the point cloud acquired by GeoSLAM ZEB Horizon RT (Figure 12) dropped by approx. 1/3 after the ICP transformation, which indicates that although a component of systematic error is present in the cloud, the random error predominates. The maximum error was the highest of all tested scanners (12 cm).  
From the perspective of the error components, the FARO Orbis scanner (Figure 13) yielded a cloud with characteristics similar to the GeoSLAM ZEB Horizon RT. Random error predominated in that cloud as well, with RMSE dropping by about 25%. The maximum error of 4 cm was, however, notably smaller than in the case of GeoSLAM ZEB Horizon RT.