# 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
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Part** & **Min** & **Max** & **Mean** & **Std** \\ \hline Figure 18a & 0.0105 & 3.1606 & 0.6576 & 0.3838 \\ Figure 18b & 0.0171 & 1.1075 & 0.3469 & 0.1761 \\ Figure 18c & 0.0068 & 3.1068 & 0.8512 & 0.7559 \\ Figure 18d & 0.0182 & 2.2808 & 0.3487 & 0.1715 \\ Figure 18e & 0.0496 & 0.2346 & 0.2243 & 0.0070 \\ Figure 18f & 0.0109 & 0.0125 & 0.1146 & 0.0046 \\ Figure 18g & 0.0171 & 1.8814 & 0.3311 & 0.1806 \\ Figure 18h & 0.0156 & 7.3595 & 0.4190 & 0.3115 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Inspection results (mm). Figure 19: The histogram of inspection results in Figure 3. (**a**)–(**h**) are a series of inspection results histogram of vehicle body parts in Figure 3, respectively. The histogram of point cloud deviation can be seen in Figure 20. The deviation distribution of point cloud tends to be larger under the HS algorithm. This reflects that, although the deviation of parts has a manufacturing error, the algorithm selected also greatly affects the evaluation results of parts in the inspection process. Therefore, the vehicle part inspection workflow in this paper provides a reliable method for vehicle body quality inspection. ## 5 Conclusions and Future Work

Under the background of \"Industry 4.0,\" it was found that the whole vehicle factory interfered in the measurement and matching of vehicle parts, resulting in the reworking of parts and assembly failure, which greatly increased the workload of engineers and technicians and reduced production efficiency. Therefore, using optical non-contact measurements and computer science technology, combined with an analysis of the research status of virtual matching technology at home and abroad, we proposed an inspection technology for vehicle body parts based on point cloud data. Optical measurement equipment is used as a tool based on the measured point cloud data of the acquired parts. Firstly, the point cloud noise data were removed by the hybrid filtering algorithm and the target point cloud data were extracted. Secondly, to reduce the subsequent calculation workload and improve the efficiency of the analysis, a fuzzy clustering simplification algorithm based on feature retention of the PCD was designed. Thirdly, in combination with the CAD model data of the parts, the SAC-IA and TLBO algorithms for point cloud data were used from the coarse registration to the registration process to complete the complete data alignment operation. In the final analysis after pretreating the actual state PCD for the parts, this paper offers an effective technical proposal for an examination process of automobile body parts. In future work, we will continue to carry out quantitatively research the matching of vehicle parts, especially when the interference is found in the parts, the interference value is obtained in advance through the related algorithm design to pre-adjust the parts and reduce the assembly error rate. In addition, the influence of point cloud noise should be further reduced during the point cloud pretreatment process, so future work is also needed in this area. Algorithm design and Writing--original draft, Y.Y. ; Writing--review and editing, M.L. ; Funding acquisition--X.M. All authors have read and agreed to the published version of the manuscript. This paper is supported by the Natural Science Foundation of Ningbo, China (Grant No. 2016A610039) and Major Research Plan of Ningbo, China (Grant No. 201601 ZDA01097). Figure 20: The inspection results in Figure 2h. **Acknowledgments:** The author thanks the editors and anonymous reviewers for their constructive comments on the manuscript and for the basic research of relevant scholars in the references. **Conflicts of Interest:** The authors declare no conflict of interest. ## Appendix A

\[\mathbf{ax}+\mathbf{by}+\mathbf{cz}= \tag{11}\] \[C=\left[