## 1 Introduction  
Recently, time-of-flight (ToF) cameras have been used in various applications in industrial settings. Due to their laser projection structure, these ToF cameras are less dependent on environmental lighting conditions and provide high-performance 3D measurement results, making them crucial in fields such as industrial robots, autonomous vehicles, and industrial measuring equipment. However, ToF cameras still have areas that need improvement. Their performance can significantly decrease due to environmental factors such as the internal temperature of the camera, ambient temperature, and humidity, which can affect the accuracy of distance measurements. Additionally, the accuracy of ToF cameras varies significantly with the distance to the target, requiring correction technologies for practical applications [1, 2]. To address these issues, various studies have been conducted. Efforts to improve performance have been made through enhancements to the sensors and lens structures within ToF cameras, advancements in software technologies, and improvements in calibration algorithms. In particular, recent research has focused on improving the performance of ToF cameras using deep learning technology. Deep learning has shown high performance in the field of image processing and can be applied to ToF camera sensor data for correction and filtering [3]. Research on improving the performance of ToF cameras has been ongoing, and recently, as the application possibilities of ToF cameras in industrial robots, autonomous vehicles, and industrial measuring equipment have expanded, the demand for more efficient sensor technologies has increased. Industrial robots have primarily been used to perform uniform tasks in automated production lines, playing a significant role in enhancing productivity and efficiency in manufacturing plants. However, there is a rapid trend of transitioning from industrial robots to mobile robots due to reasons such as productivity, efficiency, safety, and labor savings. Unlike industrial robots, mobile robots are designed to work with humans, prioritizing safety and flexibility over repeatability. As the use of robots expands, mobile robots require high positional repeatability and precise manipulator control. Industrial robots guarantee highly accurate position control and repeatability, typically with repeatability less than 0.01 mm. In contrast, mobile robots, which operate in environments alongside humans, are designed to function more flexibly and safely, usually having repeatability in the range of 20 mm to 50 mm [4]. Due to the lack of positional repeatability and accuracy in mobile robots or mobile manipulators, there are cases where they cannot be applied. To address these issues, this paper proposes a new method to improve the pixel-level sensing accuracy of ToF cameras by obtaining accurate ground truth distance values from depth data acquired in real-world environments using ToF cameras. To obtain accurate ground truth distance values from depth data acquired with ToF cameras used in real-world environments, a simulation environment was constructed with the same ToF sensor viewpoint as the real environment, and the simulation was synchronized with the real environment to acquire depth values for 3D parts. It has been demonstrated that the accuracy of the ToF camera's depth data can be improved through AI learning using the accurate depth values obtained from the ToF camera [3]. Using the method proposed in this paper, the accuracy of the ToF camera's depth data was improved. Based on this, it was possible to enhance inspection accuracy in automotive manufacturing processes by replacing 2D vision systems with 3D vision inspections. Furthermore, even when positional accuracy errors occur in mobile robots, the ToF depth data can be utilized to accurately correct the measurement positions of mobile manipulators.