# 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
These simplification rates can maximize the removal of redundant data points while retaining the features of the point cloud model, providing a good data foundation for subsequent point cloud processing to reduce the workload and improve the computing efficiency. ### Registration Experiment

After denoising and simplifying of the PCD, the PCD was registered with its CAD model. The registration process was first performed through coarse registration based on SAC-IA and placed in a good initial position. Then the fine registration process based on TLBO was used to complete the whole point cloud registration process. The parameters of the registration process were set as follows: \(T=500\), \(U=100\), \(L=100\), \(N=30\), \(D=6\). The registration process is shown in Figures 11-17 where the data (yellow) is the PCD of CAD model and the blue data comprise the simplified measurement PCD. Figure 11: PCD after registration in Figure 10a. (**a**) is the initial position of the point cloud, (**b**) is the result of coarse registration, and (**c**) is the result of fine registration. Figure 14: PCD after registration in Figure 10d. **(a)** is the initial position of the point cloud, **(b)** is the result of coarse registration, and **(c)** is the result of fine registration. Figure 12: PCD after registration in Figure 10b. **(a)** is the initial position of the point cloud, **(b)** is the result of coarse registration, and **(c)** is the result of fine registration. Figure 13: PCD after registration in Figure 10c. **(a)** is the initial position of the point cloud, **(b)** is the result of coarse registration, and **(c)** is the result of fine registration. Figure 8:0 PCD after registration in Figure 10f. **(a)** is the initial position of the point cloud, **(b)** is the result of coarse registration, and **(c)** is the result of fine registration. From Figures 11-17, (a) is the initial PCD location, (b) is the PCD location after coarse registration and (c) is the PCD location after fine registration. From the data fusion state of PCD, we can see that the fusion result of point cloud and CAD model is more uniform after fine registration, while the coarse registration result is more biased but it provides a good initial position for the calculation process of fine registration and avoids the process of fine registration from falling into the local optimal prematurely. Take the parts in Figure 11a as an example, the spatial transformation matrix from the coarse to fine registration process is shown as Equation (31). Table 3 is the registration parameters of PCD in this paper. \[\left[ \begin{array}{ccccc}-0.014&0.006&1.000&1196.752\\ 0.998&0.066&0.014&-1590.586\\ -0.066&0.998&-0.007&460.298\\ 0.000&0.000&0.000&1.000\\ \end{array} \right]\rightarrow\left[ \begin{array}{ccccc}1.000&-0.011&0.001&-6. 414\\ 0.011&1.000&-0.009&-9.384\\ -0.001&0.009&1.000&6.170\\ 0.000&0.000&0.000&1.000\\ \end{array} \right] \tag{31}\]

Figure 16: PCD after registration in Figure 10g. (**a**) is the initial position of the point cloud, (**b**) is the result of coarse registration, and (**c**) is the result of fine registration. Figure 17: PCD after registration in Figure 10h. (**a**) is the initial position of the point cloud, (**b**) is the result of coarse registration, and (**c**) is the result of fine registration. ### Inspection Results

According to the above analysis process of PCD, the initial PCD were denoised, simplified and registered, respectively. The PCD after the pre-processing is further analyzed, mainly including the dimensional deviation of the part itself and the matching state analysis with other parts by KNN. The inspection results are shown in the Figures 18 and 19. Figure 18 shows the color deviation diagram of the inspection result. Figure 19 shows the histogram of the inspection results.