## 6 Conclusions  
This study utilized time-of-flight (ToF) sensors, which are cost-effective depth measurement sensors, for various applications in industrial settings where high accuracy and precision are required. To achieve this, a 3D model identical to the real environment was created in the simulation setting, and the position and viewpoint of the ToF sensor were aligned accordingly. This setup enabled the capture of pixel-specific distance values, which were previously challenging to obtain, and these values were used as labels for training data in deep learning processes. As a result, the AI algorithms developed were able to achieve data accuracy comparable to traditional methods, laying the groundwork for the development of higher-performance AI algorithms in the future. Additionally, the study generated simulation data that could replace the high-precision devices typically used for evaluating ToF sensor performance, and this data can be used as ground truth for ToF sensor performance evaluations. While not fully meeting all the varied application needs of the industrial sector, the enhanced accuracy and precision of the ToF sensors have demonstrated their potential in applications such as vision inspection by mobile robots. Recently, many companies have been conducting research to enhance productivity using various robotic platforms including mobile robots, industrial robots, and humanoids, with a focus on autonomous operations facilitated by AI technology. A crucial part of this research involves acquiring and understanding real-time distance information about the surrounding environment. For this purpose, a compact sensor capable of economically  
Figure 12: Vision inspection using images from 2D RGB cameras and vision inspection using 2D/3D depth mixed images and depth information.  
acquiring real-time depth data of the environment and objects is essential. The authors anticipate that ToF sensors will fulfill this role effectively.  
Conceptualization, C.Y. and J.K.; Software, C.Y.; Validation, C.Y.; Formal analysis, J.K.; Investigation, C.Y.; Resources, J.K.; Writing--original draft, C.Y. and J.K.; Writing--review and editing, D.-S.E. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Hyundai Motor Company under project number 2023_CSTG_0121.  
Not applicable.  
The data presented in this study are available on request from the first author due to Hyundai Motor Company's internal policies and therefore are not publicly available. Data access requests can be directed to the first author.  
The authors declare that they have no known competing financial interests or personal relationships that could appear to influence the work reported in this paper. However, [PERSON] is employee of Hyundai Motor Company, which provided funding and technical support for this work. The funder had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ToF & Time of Flight \\ AI & Artificial Intelligence \\ RGB-D & Red, Green, Blue-Depth \\ ICP & Iterative Closest Point \\ CNN & Convolutional Neural Network \\ SOR & Statistical Outliers Removal \\ IQR & Interquartile Range \\
3D & Three-Dimensional \\ RGB & Red, Green, Blue \\ SLAM & Simultaneous Localization and Mapping \\ Q1 & First Quartile \\ Q3 & Third Quartile \\ \end{tabular}