## 3 Results
### Overall Registration of TLS and HLS Point Cloud for Simulated Mining  
In our study, the Trimble X9 terrestrial laser scanning and Zeo Horizon handheld laser scanning system were used to collect the point cloud of the simulated mine, which is shown in Figure 3a,b. The sizes of the point clouds were 49,613,211 and 83,372,881 points, respectively. Scanner parameters and test datasets are summarized in Table 1.  
Figure 3: Experiment data: (**a,b**) simulated mining point clouds collected by TLS and HLS systems, respectively. Figure 2: Study areas: (**a**) simulated mine; (**b,c**) the working face and roadway of the real coal mine, respectively. As displayed in Figure 3, the huge amount of point cloud data inevitably contains noisy points, such as points that are outside the scan setting range, do not belong to the study object, and are caused by external environments (vibration, wind, and temperature). In our study, point cloud data processing software was used to remove noisy points by human-computer interactions. In addition, to ensure the efficiency of the algorithm, the filtering algorithm based on a voxel grid was carried out at a spacing of 3 cm, and the amount of downsampling point cloud data were 4,290,495 and 4,849,716, respectively. Based on the TLS scanning principle, it adopts a self-defined coordinate system that is inside the instrument, and the coordinate origin is located in the center of the laser scanner. In addition, in the underground mine space, the laser SLAM point cloud captured by the HLS system is also located in the free coordinate system due to the absence of GNSS signals. Therefore, the initial positions of the SLAM and TLS point cloud coordinate bases differ significantly, which is shown in Figure 4a, where the yellow and green points represent the TLS and HLS point clouds, respectively. In the simulated mining data acquisition experiments, since the control point information could not be obtained, the corner points from the multi-platform point cloud were extracted by point cloud data processing software. As a result, the HLS point cloud was approximately transformed into the coordinate system where the TLS point cloud is located, which is presented in Figure 4b, where the registration accuracy is 16 cm. However, the scene features of the simulated underground mining are not obvious and the alignment accuracy is poor, with only three extracted feature points. It should be noted that TLS offers higher accuracy than HLS, but aligning data from two systems with different precision levels can result in misalignment and inaccuracies in the final fused point cloud. The difference in accuracy becomes more pronounced in \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Simulated Mine**} \\ \cline{2-3}  & \multicolumn{1}{c}{**TLS Point Cloud**} & \multicolumn{1}{c}{**HLS Point Cloud**} \\ \hline Number of scans & 22 & — \\ Accuracy & Range & 2 mm & 3 cm \\ Angle & \(<\)16\({}^{\circ}\) & — \\ Number of points & & 49,613,211 & 83,372,881 \\ Uniform downsampling & & 4,290,495 & 4,849,716 \\ Point density (points/10 cm\({}^{2}\)) & 705 & 767 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the datasets. Figure 4: The registration results of HLS and TLS point clouds for simulated mining. **(a)** Multi-platform point cloud initial position; **(b)** coarse registration result of multi-platform point cloud based on extracted feature points; **(c)** identical area extraction results of TLS and HLS point cloud; **(d)** fine registration result of HLS and TLS point clouds based on the INDT algorithm. confined or irregular underground spaces, where slight errors can lead to significant deviations over the entire dataset. Therefore, after the coarse registration of the multi-platform point cloud, the axis vector angle corresponding to the minimum moment of inertia was calculated to determine the point cloud distribution similarity between the point clouds of the two platforms, and the identical areas were searched.