## 4 Discussion and Conclusions  
In our study, an overall registration method for a multi-platform heterogeneous laser point cloud for underground mine engineering is proposed, and two main improvements were implemented. For underground mines, the multi-platform point cloud registration method has not been discussed in previous research. Terrestrial laser scanning and handheld laser scanning systems were adopted to collect point clouds of an underground mine. The spatial reference, observation viewpoint, and data quality of the multi-platform point clouds were so different that it was difficult to strictly establish point-to-point correspondence. Therefore, the coarse-to-fine registration method was used to transform the HLS point cloud to the coordinate system located by the TLS point cloud, and roughness was considered when downsampling the fusion point cloud.  
To analyze the registration advantage of the proposed method, pairwise local registration with global optimization, feature-based registration with iterative closest-point refinement, and machine learning-based alignment were considered. The advantages and challenges of each approach compared to an overall registration method are presented in Table 3.  
Although the proposed method can achieve overall registration of multi-platform point clouds, there are still some limitations to be considered. On one hand, in the underground mine, the inertial measurement unit of the HLS system generates drift over time due to similar environmental characteristics, and the point cloud position correction algorithm needs to be discussed in subsequent research. On the other hand, the 3D laser scanning technology has a low single-point positioning, and it is difficult to obtain repeated observations of fixed measurement points. Traditional measurement techniques such as a total station can be used as a supplement when obtaining deformation information at fixed locations in underground mines.  
Conceptualization, W.S. and J.W.; methodology, W.S.; validation, W.S. and X.Q.; formal analysis, W.S.; data curation, W.S. and Z.L.; writing\(-\)original draft preparation, W.S.; writing\(-\)review and editing, W.S.; supervision, J.W. and F.J.; project administration, J.W. and F.J.; funding acquisition, W.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant number 42401536 and Shandong Provincial Natural Science Foundation under grant number ZR2024 QD070.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Challenges** \\ \hline Pairwise local registration with global optimization & Reduces error propagation, scalable, adaptable to local geometry & Requires overlapping points, needs post-processing optimization \\ Feature-based registration with ICP & Accurate for distinct geometries, low initial alignment sensitivity & Feature scarcity in uniform areas, noise sensitivity \\ Machine learning & Noise-resistant, adaptable, fast processing post-training & High data/training needs, costly to train, generalizability issues \\ Proposed method & Simplifies workflow, requires minimal initial alignment & Computationally intensive, inconsistent \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of four registration methods.