# An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data
## 3 Materials and Methods
### Methods
#### 3.2.2 Denoising of PCD Based on a Hybrid Filtering Algorithm
Figures 18 and 19 show the inspection results of four groups of vehicle body parts based on the denoised PCD and the spatial matrix registered, where Figure 18 is the dimensional deviation of the single part and Figure 19 the deviation histogram of vehicle parts. Take Figure 18a as an example, the minimum, maximum, mean and standard deviation of part deviation are 0.0105 mm, 3.1606 mm, 0.6576 mm and 0.3838 mm in Table 4, respectively. The inspection results of other vehicle parts are also shown in Figures 18 and 19 and Table 4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Part** & \(\alpha\) & \(\beta\) & \(\gamma\) & \(d_{x}\) & \(d_{y}\) & \(d_{z}\) \\ \hline \multirow{6}{*}{Coarse registration} & Figure 11 & 9.0387 & 3.793 & 90.823 & 1196.752 & \(-\)1590.586 & 460.298 \\  & Figure 12 & \(-\)65.745 & \(-\)20.340 & \(-\)94.093 & 1166.474 & 426.493 & 314.908 \\ \cline{1-1}  & Figure 13 & 177.283 & 2.508 & 173.269 & 2132.986 & \(-\)728.748 & 291.238 \\ \cline{1-1}  & Figure 14 & 138.172 & \(-\)77.432 & 143.629 & 1299.793 & \(-\)697.308 & \(-\)732.713 \\ \cline{1-1}  & Figure 15 & 146.283 & 5.299 & 100.222 & 2432.986 & \(-\)511.439 & 543.314 \\ \cline{1-1}  & Figure 16 & 155.333 & \(-\)54.520 & 166.877 & 1115.545 & \(-\)555.358 & \(-\)753.234 \\ \cline{1-1}  & Figure 17 & \(-\)88.344 & \(-\)9.453 & \(-\)13.342 & 2563.111 & \(-\)657.546 & 300.455 \\ \hline \multirow{6}{*}{Fine registration} & Figure 11 & 0.500 & 0.079 & 0.685 & \(-\)6.414 & \(-\)9.381 & 6.173 \\  & Figure 12 & 8.161 & 0.056 & \(-\)0.165 & 1.157 & 7.854 & 7.963 \\ \cline{1-1}  & Figure 13 & 0.132 & \(-\)1.342 & 6.782 & \(-\)4.144 & \(-\)1.451 & \(-\)2.137 \\ \cline{1-1}  & Figure 14 & 1.397 & \(-\)0.537 & 2.823 & \(-\)2.060 & \(-\)5.086 & 1.711 \\ \cline{1-1}  & Figure 15 & 3.445 & 0.043 & \(-\)0.133 & 0.160 & 3.444 & 2.323 \\ \cline{1-1}  & Figure 16 & 0.111 & \(-\)0.554 & 2.820 & \(-\)1.155 & \(-\)0.775 & \(-\)1.444 \\ \cline{1-1}  & Figure 17 & 1.337 & \(-\)0.666 & 2.823 & \(-\)1.041 & \(-\)2.168 & 0.911 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Registration parameters. Figure 18: The color deviation diagram of inspection results in Figure 3. (**a-h**) are a series of inspection results color deviation diagram of vehicle body parts in Figure 3, respectively. From the point cloud deviation of the above parts shown in Figure 18a-h, although the maximum and minimum values are relatively large, their mean values and standard deviations are within the standard value range. This is due to the large amount of point cloud data, the processing process is very complex, there may be some individual noise points, affecting the results of the maximum and minimum values. Therefore, the mean value and standard deviation of statistical data have more practical guiding significance. Therefore, in order to more objectively reflect the actual state of the measured data, the deviation histogram is shown in Figure 19a-h. It can be seen from the figure that, except for Figure 19c, the deviation distribution of the other point cloud is relatively uniform, indicating that the algorithm designed has a good computing effect and can accurately complete the point cloud pre-processing process and carry out the actual state analysis of the point cloud. As for the deviation histogram of vehicle body part shown in Figure 18c, some points have large deviation (1.5 mm\(\sim\)2.5 mm), so it is necessary to further inspect and analyze whether the dimension of parts meets the design requirements. Finally, we compare this algorithm framework with other algorithms. Considering that any link in the inspection process will have an impact on the calculation results, in this test, TLBO algorithm in the process of fine registration is replaced by the HS algorithm, while the rest of the calculation process remains unchanged. The vehicle part in Figure 2h is taken as the research object. The deviation histogram of the two algorithm framework is shown in Figure 20.