## 5 Conclusions and Outlook  
To meet the intelligent obstacle avoidance needs of high-speed rail tunnel detection vehicles during lining quality detection, this article, through software and hardware design and development and on-site experimental verification, arrived at the following conclusions:  
1. The point cloud registration and densification method integrating ORB-SLAM3 and NDT technology can detect small-volume obstacles with a diameter greater than 0.5 cm in high-speed rail tunnels in real time, improving the efficiency and safety of tunnel detection.
2. The obstacle risk detection and location method based on the safety limit model has shown effectiveness and feasibility in on-site tests, with obstacle avoidance errors between 1 and 2 cm, meeting design accuracy requirements. This provides higher accuracy and robustness for obstacle identification in complex tunnel environments.
3. The multi-modal data fusion scheme based on time synchronization and spatial registration can visually observe obstacles, providing an effective early warning function for the safe operation of high-speed rail.
4. In the future, we will improve the accuracy and real-time performance of the intelligent tunnel detection system, provide strong support for building more powerful automated tunnel detection control systems, and digital twin lifecycle analysis technology for railway tunnels.  
Conceptualization, Y.L. and T.T.; methodology, Y.L. and B.J.; software, Y.L.; validation, Y.L. and T.T.; formal analysis, Y.L. and F.J.; investigation, T.T. and Q.Q.; data curation, Y.L. and F.Q.; writing--original draft preparation, Y.L. and T.T.; writing--review and editing, Y.L. and B.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Project of Science & Technology Research of Infrastructure Inspection Research Institute, grant number 2021 JJXM23, and the Key Project of Science & Technology Research of the China Academy of Railway Sciences, grant number 2021 YJ253.  
Not applicable.  
The original data contributions presented in the research are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest. The funders did not play a role in the design of the study, the collection or the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Obstacle**} & \multirow{3}{*}{**Base Value (cm)**} & \multicolumn{6}{c}{**Distance from Obstacle to Envelope (cm)**} & \multirow{3}{*}{**Obstacle Avoidance**} \\ \cline{3-3} \cline{5-8}  & & \multicolumn{3}{c}{**Pre-Avoidance System Alarm Value**} & \multicolumn{3}{c}{**Post-Avoidance System Output Value**} & \multicolumn{1}{c}{**Avoidance**} \\ \cline{3-3} \cline{5-8}  & & \multirow{2}{*}{**3-s Average**} & **3-s Standard** & **Average** & **3-s Average** & **3-s Standard** & **Average** & **Action** \\ \cline{1-1} \cline{6-8}  & & & **Deviation** & **Error** & **Deviation** & **Error** & **Error** & **Deviation** \\ \hline tunnel wall & \multirow{2}{*}{5.0} & \multirow{2}{*}{5.3} & \multirow{2}{*}{0.19} & \multirow{2}{*}{0.3} & \multirow{2}{*}{26.7} & \multirow{2}{*}{1.32} & \multirow{2}{*}{1.7} & arm shrinks \\ pipeline & & & & & & & & & 20 cm \\ \cline{1-1} \cline{6-8}  & & & & & & & & & \\ \cline{1-1} \cline{6-8}  & & & & & & & & & 10 cm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical arm obstacle avoidance test results.