# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 6 Conclusions  
The missing 3D point cloud not only affects the display effect but also has a great impact on the subsequent point cloud data processing. Therefore, it is necessary to repair the 3D point cloud data to ensure the integrity and authenticity of the point cloud data. The underground space of mines (such as goaves, access shafts, etc.) is narrow and hidden, so it is difficult to collect complete data by using a single data acquisition method. To solve this problem, this paper used the RGB-D depth camera to repair the hole caused by the missing three-dimensional laser scanning point cloud. RGB-D camera is smart and convenient for data acquisition in the narrow roadway and can collect roadway data in a small range. We used the ICP algorithm to align each key frame of the point cloud data collected by RGB-D camera. The maximum error of the point cloud constructed by two different devices was 2.617 cm, and the minimum was 0.031 cm, which can satisfy the visual repair of the missing parts of the 3D laser scanner data collection. The obtained RGB-D camera point cloud and 3D laser scanning hole point cloud could be repaired by the algorithms of FPFH + ICP, ISS + ICP, SVD + ICP, or 3D-NDT. Our study results indicated that the ISS + ICP registration algorithm performed better than others, with the number of point clouds participating in RMSE calculation being 28,153 and an RMSE value of 13.8524 mm. Its relative accuracy meets the requirements of visual repair. The repair results show that the two-point clouds fit well, which not only met the requirements of hole repair, but also maintained the original characteristics of the underground access shaft roadway for quantitative analysis.  
Conceptualization, H.T. and Y.X.; validation, H.T., C.L. and M.Y. ([PERSON]); formal analysis, C.L.; resources, X.H. and Y.X.; data curation, X.K.; writing--original draft preparation, H.T.; writing--review and editing, X.W.; project administration, M.Y. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 41861054).  
The data used to support the findings of this study are presented in the article. Further inquiries can be directed to the corresponding author.  
The authors declare that they have no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **Registered RMSE** & \begin{tabular}{c} **Total Points of** \\ **Point Cloud Data** \\ **(RGB-D/TLS)** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Points of Point Cloud** \\ **Data** \\ **Involved in RMSE Calculation** \\ \end{tabular} &
\begin{tabular}{c} **RMSE** \\ **(mm)** \\ \end{tabular} \\ \hline FPFH + ICP & 0.003 & 1,388,470/726,820 & 27,057 & 19.0657 \\ ISS + ICP & 0.003 & 1,388,470/726,820 & 28,153 & 13.8524 \\ SVD + ICP & 0.003 & 1,388,470/726,820 & 27,251 & 22.3248 \\
3D-NDT & 0.003 & 1,388,470/726,820 & 27,449 & 31.9413 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison table of repair accuracy by different registration algorithms.