## 6 Discussion
Researchers also used the MLS method in a study to create a high-resolution digital elevation model (DEM) of a leaching field. Another survey was conducted to characterize the snow surface, which is necessary to verify aerial and satellite snow measurements. In this application, dense point clouds were acquired using the ROAMER scanner mounted on a snowmobile. For monitoring Arctic permafrost landforms in relation to climate change, the mobile laser scanner Akhka was used, carried on the back of the operator during the measurement. The Akhka system allows for extremely high point densities with an absolute accuracy level of 20 mm. The authors in [49] conducted data collection using the Riegl VMX-2 HA laser scanning system, utilizing 27 GCPs and over 100 CPs during the measurement process. The scanning took place in the city of Plzen, Czech Republic, in an urban environment characterized by tall buildings and numerous trees. The laser scanner was mounted on a vehicle. The data were collected at a speed of 20 km/h, resulting in a point density of 10,250 points/m\({}^{2}\), and at a speed of approximately 35 km/h, where the point density was 5860 points/m\({}^{2}\). A total of nine point clouds were created. The analysis in this article shows that a higher speed, and thus a lower point cloud density, does not negatively affect the accuracy of the resulting point cloud. The authors also compared the accuracy of the GCPs and CPs based on GNSSand MLS measurements. From the comparison of the deviations of GCPs measured with a GNSS RTK and the deviations of CPs measured with MLS, it can be concluded that the MLS method in urban areas is more accurate than the GNSS RTK method. Further research [50] has focused on testing the performance of the SLAM scanner Zeb Horizon by GeoSLAM for creating a digital model of a bridge structure. The authors used the TLS method with the static scanner Leica ScanStation P40 as a reference method. Both methods were georeferenced to the same coordinate system using GCPs. Two point clouds of the lower bridge structure were obtained using the MLS method, containing 19,605,824 and 8,170,880 points, respectively. The data collected with the SLAM scanner had relatively high noise, with an RMSD of 0.02 m. The lower bridge structure was scanned using the Leica ScanStation P40 from 15 scan positions with a resolution of 6 mm/10 m, using a total of 7 GCPs and 21 CPs. The RMSD for the TLS method reached an accuracy of 0.005 m, and the resulting point cloud contained 39,078,802 points. A more detailed analysis was then conducted, calculating the RMSD for several profiles of the bridge structure. This analysis revealed that the deviations of the SLAM data from the reference TLS data did not exceed 0.03 m in the longitudinal, transverse, or vertical directions. Article [51] focuses on evaluating the geometric properties of point clouds collected from various commercial and research MLS systems under good GNSS conditions at an established urban test site in Espoonlahti, located about 16 km west of Helsinki. The reference method employed was TLS using the static laser scanner ROAMER, which provided an accuracy of 11 mm. The TLS was verified using 150 CPs, and a total of 9 GCPs were measured. The MLS data were collected using five different systems: ROAMER, RIEGL VMAX-250, Sensei, Streetmapper 360, and Optech Lynx. The mobile laser scanner was mounted on the roof of a vehicle, and the scanning took place at speeds of approximately 30-40 km/h. The comparison of the individual scanners revealed that high-quality point clouds could be generated by all systems under favorable GNSS conditions. With all professionally calibrated systems, the elevation accuracy was better than 3.5 cm up to a range of 35 m. The best system achieved a horizontal accuracy of 2.5 cm even at a range of 45 m.  
Publication [52] discusses the methodology for assessing the accuracy and correctness of MLS using advanced statistical methods. The test area was the historic center of Trento in Italy.