## 5 Conclusions  
This paper presents an optical in-service detection method for determining the surface deformation and straightness of an electromagnetic launcher. By utilizing ring-structured light scanning, time-of-flight ranging, and laser alignment, the point cloud data of the bore are obtained. Through registration with the standard profile point cloud, the surface deformation distribution of the rails and insulators and the straightness of side-bending and arch-bending are detected. The analysis reveals that increasing the wheelbase can mitigate the accuracy issues caused by the pitching and yawing of the detection device itself within the bore, whereas the rolling issue needs to be addressed through point cloud registration. Notably, the coarse registration of the rail arc features based on the combination of the circle and parabola equations can significantly enhance the overall registration efficiency, and the shortest registration time can be obtained by using only simple least-squares fitting. Additionally, the fine registration algorithm based on TrICP has more accurate results than the ICP algorithm in the non-axisymmetric bore deformation scenario. These experiments show that the bore surface deformation and straightness can be detected efficiently and precisely using the proposed method. During the detection process, the post-processing of data registration can be completed in real time. The test accuracy reaches 22.6 \(\pm\) 6.3 \(\upmu\)m in a simulated environment with an inner bore undulation of 5 mm.  
It should be acknowledged that this paper does not delve deeply into the impact of body structural vibrations and deformations during the movement of the detection device. Predictably, such vibrations and deformations can disrupt the accuracy of the  
Figure 11: Bore profile point cloud registration results under the non-axisymmetric deformation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Coarse Registration Method**} & \multicolumn{2}{c}{**Registration Computation Time (s)**} \\ \cline{2-6}  & **Rotation** & **Translation** & **Coarse** & **Fine** & **Total** \\ \hline
1 & – & – & 0.00 & 101.23 & 101.23 \\
2 & LS-circle & LS-circle & 0.23 & 57.32 & 57.55 \\
3 & RANSAC-circle & RANSAC-circle & 0.66 & 44.13 & 44.79 \\
4 & RANSAC-circle & LS-parabola & 0.78 & 14.42 & 15.20 \\
5 & RANSAC-circle & RANSAC-parabola & 1.14 & 14.34 & 15.48 \\
6 & LS-circle & LS-parabola & 0.23 & 14.51 & 14.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of point cloud registration computation time (fine registration using TrICP algorithm).  
pre-calibrated extrinsic matrix parameters, thereby inhibiting further improvements in detection resolution. In future work, this issue will be investigated through full-trolley structural dynamics simulations that take into account the effects of assembly gaps, and effective methods for vibration suppression will be explored.  
Methodology, K.Y.; validation, L.C.; formal analysis, S.T.; investigation, K.Y.; data curation, D.Z.; writing--original draft, K.Y.; writing--review and editing, L.C.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 92166108 and 52207070.  
Not applicable.  
The data presented in this study are available upon reasonable request from the corresponding author. The data are not publicly available due to privacy reasons.  
The authors declare no conflicts of interest.