## 5 Discussion and Future Work  
Currently, augmented reality technology is widely used in industry. Most of the existing AR development tools rely on time-consuming and expensive recompilation steps, which are costly and lack versatility. This study proposes a quick development toolkit named QDARV for AR visualization of factories, which can help developers quickly develop AR applications for factory visualization to improve industrial production efficiency. The QDARV tool consists of AR configuration software and script parsing and AR display software. The AR configuration software generates intermediate script files through the 3D animation configuration module, the AR display configuration module based on QR code, and the AR display configuration module based on SLAM. The script parsing and AR display software reads and parses the intermediate script files to generate AR display content. On this basis, AR registration based on QR code and AR registration based on SLAM map reloading are realized. With the QDARV toolkit, developers do not need a deep understanding of AR technology or extensive programming experience. They only need to configure augmented reality display content and augmented reality registration parameters to generate an intermediate script file, thereby achieving data communication between the AR configuration software and script parsing and AR display software. This helps to design industrial AR applications, customize the display of augmented reality content, and achieve a human-centered display. Compared with other AR tools, the QDARV tool developed in this paper does not rely on time-consuming and costly recompilation steps, and therefore AR display content and AR applications can be created quickly and efficiently. The QDARV tool provides disassembly and assembly animation configuration and virtual instrument configuration functions. With these functions, developers can quickly realize disassembly and assembly animation AR display and industrial instrument AR display in real scenes. In addition, the AR registration method is improved based on ORB_SLAM2. In this improved version, the functions of map saving and map loading are added, which can save and reload world data. Meanwhile, a semantic thread is added in this improved version to track the pose of 3D objects in the scene and establish an object-oriented semantic map, thereby improving the robustness and tracking performance of the system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Question** & **Researchers** & **Operators** & **Mean** \\ \hline Q1 & 8.8 & 8.3 & 8.55 \\ Q2 & 9.0 & 8.6 & 8.8 \\ Q3 & 9.5 & 9.6 & 9.55 \\ Q4 & 8.4 & 9.0 & 8.7 \\ Q5 & 6.5 & 6.0 & 6.25 \\ Q6 & 8.3 & 8.8 & 8.55 \\ Q7 & 9.1 & 9.3 & 9.2 \\ Q8 & 9.2 & 8.6 & 8.9 \\ Q9 & 6.2 & 6.5 & 6.35 \\ Q10 & 6.1 & 6.7 & 6.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The user evaluation results of the QDARV development tool. Section 4 validates the effectiveness, feasibility, and advantages of the proposed tool through experiments and analysis. Nonetheless, the QDARV tool has some limitations that need to be improved in future studies. This study only uses a monocular camera for pose estimation. The tracking of the system can be easily lost when the AR device moves or rotates rapidly. Meanwhile, the IMU sensor can estimate fast motion in a short time and estimate the scale through its inertial measurements. The fusion of the monocular camera and IMU sensor will be considered in future work to improve the robustness and localization performance of the system under fast motion. In addition, at present, the QDARV tool is only applicable to Windows development platform and has not been integrated with other industrial software. In the future work, we will continue to improve the functions of the QDARV tool to support cross platform development and integration with industrial software (SCADA, databases, cloud platforms, etc.). Conceptualization, C.C., Y.P. and D.L. ; Data curation, Z.Z. ; Funding acquisition, C.C. ; Methodology, C.C., R.L. and D.L.