## 4 Discussion  
We surveyed a bridge structure using a high-accuracy static scanner and used the thus acquired cloud to evaluate those produced by a SLAM scanner Zeb Horizon by GeoSLAM to determine the accuracy that can be achieved by this type of scanner. The bridge structure was selected for the survey because of its complex shape with numerous projections and irregular shapes. To assess the effect of data processing on the results, we also used MLS data smoothing. In addition, to remove the influence of possible georeferencing errors, we also used the ICP algorithm to better illustrate the local accuracy of the data. The SLAM scanner data were characterized by much greater noise than the static scanner cloud. On the other hand, SLAM scanners can achieve better area coverage as the scanner location keeps moving, which eliminates obstructions. The shape accuracy of the resulting cloud remains a crucial parameter of the acquired cloud. Importantly, it often depends on the correct function of the algorithm in the respective environment. Many studies have investigated the accuracy of the SLAM technology; unfortunately, few are truly conclusive. Testing of SLAM technology has been addressed, for example, in [31], where the quality of point clouds acquired by different SLAM algorithms was evaluated. The achieved accuracies of the results were several centimeters to decimeters; however, these accuracies were simply obtained by comparing distances measured in the field to distances measured in the cloud. Given the laborious nature of field distance measurements, this approach led to a number of distances insufficient for a reliable statistical evaluation. Similarly, [PERSON] et al. [35] compared the results of mapping using SLAM scanners mounted on robotic rovers, employing also different algorithms, with the results obtained by a terrestrial scanning system. They reported an average accuracy of 0.05 m _RMSE_; however, in the case of a long narrow corridor, the deviations were around 0.1 m.  
Figure 14: Shifts of GeoSLAM 2\(-\)MLS data from the reference cloud in mm (Table 4), **(a)** shifts dX\({}_{\text{MLS}}\) and dY\({}_{\text{MLS}}\), **(b)** shift dZ\({}_{\text{MLS}}\). [PERSON] et al. [36] tested the GeoSLAM scanner for mapping subterranean spaces and reported _RMSDs_ of 5-6 cm; however, their comparison was just between two measurements with the same scanner, not with more accurate reference data. As our comparison of two passes with the scanner (see Tables 3 and 4) demonstrated, such a design is unfortunate as both passes may suffer from identical systematic errors and thus yield a good agreement between the passes even though the deviations from the reality (in our case, the reference cloud) are higher. Interesting results have been presented by [PERSON] et al. [17] who tested several technologies for georeferencing of a castle and its vicinity. Accuracies achieved using the SLAM scanner GeoSLAM REVO were in centimeters, which corresponds well to our results. [PERSON] et al. [37] discussed and tested the use of a combination of SLAM and a static terrestrial scanner for georeferencing indoor spaces. The accuracies detected in their study were also 0.05 m and better; they, however, evaluated accuracy only using several spherical targets in individual rooms of the mapped space so there were rather few points for a major statistical evaluation. [PERSON] et al. [18] scanned approximately 75 m long subterranean spaces, the walls of which were formed by chiseled stones and bricks. Three SLAM scanners were tested and compared to the results of a terrestrial scanner. They reported very good GeoSLAM Zeb Horizon accuracy (_RMSD_ of 0.017 m). However, they used local transformation of a part of the cloud to the reference data (similar to if we had transformed each profile separately to the reference data), which artificially improved the results, and the global accuracy over the entire cloud was not reported.