## 5 Results
### Analysis of the Differences in Point Cloud Based on the Shift on Objects  
This section focuses on the analysis of the accuracy and reliability of the TLS and MLS methods by evaluating the shifts on selected objects in an urban environment. For the purposes of this analysis, three types of objects were chosen, each characterized by their structure and stability: a tree, a building, and the mast of a street lamp. These objects provide different surfaces and conditions, ensuring a representative assessment of the accuracy and reliability of both technologies. The goal is to determine the differences between the TLS and MLS methods based on the accuracy of measuring these objects and to identify potential deviations that may affect the resulting point clouds. The analyses on individual objects were carried out using the Leica Cyclone 3 DR software. The first object selected for deriving differences between point clouds was a tree, specifically its trunk. The point cloud of the trunk was analyzed from TLS, the Lidaretto mobile laser scanner, and the Stonex X120 GO mobile laser scanner (Figure 10). After filtering unnecessary points, the TLS point cloud contained 6,343,281 points, the Lidaretto mobile laser scanner contained 993,205 points, and the Stonex X120 GO point cloud had 457,215 points. For each point \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Software** & **Classification Classes** & **TP** & **FP** & **FN** & **Precision** & **Recall** & \(\mathbf{F_{score}}\) \\ \hline
**Leica** & & Ground & 6,099,244 & 98,264 & 241,423 & 0.984 & 0.962 & 0.973 \\
**Cyclone** & 3 & Medium vegetation & 4,387,916 & 750,427 & 53,440 & 0.854 & 0.988 & 0.916 \\
**3 DR** & & Building & 41,468,516 & 228,719 & 182,048 & 0.995 & 0.996 & 0.995 \\ \hline
**Trimble** & & Ground & 5,438,578 & 13,813 & 569,521 & 0.997 & 0.905 & 0.949 \\
**RealWorks** & & High vegetation & 1,688,968 & 1,362,413 & 2,911,669 & 0.554 & 0.367 & 0.441 \\  & & Building & 38,536,864 & 1,931,690 & 3,161,156 & 0.952 & 0.924 & 0.938 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Quantitative evaluation of the classification results. cloud, we determined the radius of the tree trunk in the same horizontal cross-section (Table 8). The TLS trunk radius was 0.493 m; for the Lidaretto, it was 0.451 m; and for the Stonex X120 GO, it was 0.507 m. For the mobile laser scanner Lidaretto, the radius deviation from the TLS method, chosen as the reference method, was 0.042 m. For the mobile laser scanner Stonex X120 GO, the radius deviation from the reference method was 0.014 m. We also compared the coordinate deviations of the center from the reference method. For the Lidaretto mobile laser scanner, the deviation along the Y-axis was 0.006 m, and along the X-axis, it was 0.003 m. For the Stonex X120 GO mobile laser scanner, the deviation along the Y-axis was 0.016 m, and along the X-axis, it was 0.033 m.  
The next object chosen for determining the horizontal shifts between point clouds is a building. We observed shifts between the different methods on corners of the building (Figure 11). In this case, the point cloud obtained using the TLS method with a Leica RTC360 was chosen as the reference. This point cloud was compared with point clouds obtained using the Lidaretto mobile laser scanner, the Stonex X120 GO mobile laser scanner, and a combined setup of an iPhone 13 Pro with an Emlid scanning kit and a GNSS antenna Reach RX. A horizontal cross-section of the building was taken at the same height for all the point clouds. In a 2D horizontal plane, the approximated lines define corners as their intersections.