## 3 Results
### Overall Registration of the TLS and HLS Point Clouds for Coal Mining  
The coal mining working face was gradually measured with a Trimble TX8 TLS system, and a total of 13 stations were deployed (Figure 7a, each station represented by a different color). In our study, the point cloud registration of multiple stations was carried out using target spheres. The registration accuracy was 0.4 mm and the number of data points was 931,042,710. Although the TLS system has high accuracy, the data capture was incomplete due to the limitation of the field of view and occlusion of the object itself. In addition, the length of the mining roadway is about 1.7 km, and it is inefficient to use the TLS system to set up stations. Therefore, a Zeb Horizon HLS system, which offers more flexible data collection and produces a more comprehensive point cloud was used, and the number of data points was 173,095,994. Underground mines are often filled with dust, moisture, vibrations, and other factors that can affect both TLS and HLS data quality. These conditions can introduce noise into the point clouds, complicating registration and reducing accuracy, especially with HLS, which is more sensitive to movement-related distortions. Therefore, similar to the simulated mine, the real mine's data were downsampled by voxel grids (the size of the voxel grid is 1 cm), and the number of data points after downsampling was 7,025,993. For the HLS point cloud, to verify the reliability of the algorithm and simplify the calculation, only the point cloud of the working face roadway was analyzed. The number of data points overall and after downsampling was 43,480,808 and 7,562,309, respectively, where the voxel grid size of downsampling was 3 cm. Scanner parameters and test datasets are summarized in Table 2.  
Similar to the simulated mine, the initial positions of the HLS and TLS point cloud coordinate bases differed significantly, which is shown in Figure 8a, where orange and blue points represent the TLS and HLS point clouds, respectively. Furthermore, three control points are used to convert the TLS and HLS point cloud to the geodetic coordinate system. The control points position and the preliminary transform results are displayed in Figure 8b,c, respectively. It can be seen from Figure 8 that the point clouds of the two platforms can be approximately transformed to identical coordinate systems. However, due to the limitations of the operating conditions of the undergu \begin{table}
\begin{tabular}{c c c c} \hline  & & \multicolumn{2}{c}{**Real Coal Mine**} \\ \cline{3-4}  & & **TLS Point Cloud** & **HLS Point Cloud** \\ \hline Number of scans & 13 & 1 \\ Accuracy & Range & 2 mm & 3 cm \\ Angle & 80 \(\upmu\)rad & \(-\) \\ Number of points & 931,042,710 & 43,480,808 \\ Downsampling & 7,025,993 & 7,562,309 \\ Point density (points/10 cm\({}^{2}\)) & 1418 & 702 \\ \hline \end{tabular}
\end{table} 
Table 2: Description of the datasets. Figure 7: Experiment data: (**a,b**) real coal mine point clouds collected by TLS and HLS systems, respectively. control points were concentrated at the entrance of the mining working face and could not cover the entire study area, resulting in the alignment being trapped in a local optimum. The mean of the C2C distance was 38.1 cm. After the coarse registration, the axis vector angle corresponding to the minimum moment of inertia was calculated to determine the point cloud distribution similarity and the identical areas of the two-platform point cloud were searched, which is shown in Figure 8d. Furthermore, considering the advantages of the improved 3D-NDT algorithm, it was adapted to register the identical areas of the two-temporal point cloud, and the parameters were used to transform the HLS point cloud to the coordinate system located by the TLS point cloud, which is shown in Figure 8e,f. At the same time, roughness information was considered when downsampling the point cloud.