## 5 Conclusions  
Mobile mapping, a method used in surveying, faces various factors that can influence its accuracy and efficiency. The first presumed negative influence is the speed of data collection. In this work, two data collections were made with different speeds of 20 and 35 km/h. The analysis shows that higher speed, and therefore lower point cloud density, does not have a degrading effect on the accuracy of the resulting point cloud. Data collection at higher speed results in an average 2 mm better StDev XYZ. The second expected factor that affected the accuracy of the result is the configuration and quality of the control/tie points. The influence of control point spacing on the overall cloud accuracy was confirmed from only one comparison, that between a cloud with TS points spaced 50 m apart and a cloud with TS points spaced 1000 m apart. The StDev XYZ for a cloud with a point spacing of 50 m is 24 mm and 29 mm for a point spacing of 1000 m. However, for clouds from the first data collection tied to GNSS points, StDev XYZ is around 30 mm, independent of the spacing of the control points. From the maps, locations where local cloud deformations occur were identified; these are sharp turns when leaving a location with a poorer view of the sky and larger distances from the control points. This deviation can be minimised in most cases by the appropriate location of the control point. However, the influence of the quality of the points plays an important role. MLS_3 point clouds tied using a GNSS point with 50 m spacing show worse deviations than point clouds with larger spacing. This was due to the less accurate determination of the control points at locations with an obscured sky view and therefore a degradation of the quality of the GNSS RTK measurements. The results suggest that it is appropriate to select spots where the positioning of the control point will not be affected by negative influences, even at the cost of larger point spacings. The overall quality of the result can also be influenced by the ability to identify the point. Points signalled by chequerboard targets or street lines can be identified with a repeatability of 4 mm and spatial features with a repeatability of 9 mm. This error is particularly visible in the last part of the tested section, where checkpoints were harder to identify. This project has demonstrated that the highest measurement accuracy in urban areas can be achieved by using high-precision control points spaced at 50 m. Here, points with an average XYZ standard deviation of 2.2 mm were used. In this case, an average point cloud standard deviation of 22 mm was achieved. However, the making of such an accurate network is very time- and cost-consuming and in real terms inefficient and uneconomical. Using GNSS grid points, the point cloud accuracy, independent of the spacing of the grid points, was around 30 mm. In conclusion, even in the less ideal conditions in this project, the accuracy achieved in urban areas was better than or equal to the manufacturer's stated GNSS/INS positioning accuracy for ideal conditions. From the comparison of the calculated GNSS RTK checkpoint deviations and the MLS checkpoint deviations, it can be said that the mobile laser scanning method in urban areas is more accurate than the GNSS RTK method. For the above reasons, the mobile mapping method using the RIEGL VMX-2 HA system can replace GNSS measurements in urban areas to a large extent. This will speed up the measurements, save costs since a trained surveyor will not be needed to collect the data, ensure the measured data will be more detailed and wider, and ensure more information can be obtained not only about the objects of interest but also their surroundings. Despite the insights gained from this study, certain limitations must be acknowledged. This study focused on the accuracy of one type of mobile mapping device used for mapping urban build-up areas. Data collection was carried out under specific weather conditions, which can affect the results.