## 2 Related Work
### AR Registration  
The main characteristic of augmented reality visualization is that visual information has spatial relationships with the physical world. Therefore, in AR applications, AR registration is a key technology, which enables the virtual objects to be superimposed onto the real scene according to the spatial perspective relationship by tracking and locating the images or objects in the real scene. Augmented reality technology can be divided into three categories: sensor-based, vision-based, and hybrid methods. Among them, vision-based methods include marker-based and marker-less methods, and they are widely used. The marker-based AR registration method is the earliest practical and commercialized technology. The most representative research is the artificial marker-based ARToolKit [26]. The tool uses a camera to capture pre-placed markers in the scene for real-time tracking. This method has the advantages of fast tracking speed and high accuracy, and is widely used in industrial augmented reality. [PERSON] et al. [30] provided a universal calibration tool of marker-object offset matrix for marker-based industrial augmented reality applications. The method can automatically calculate the offset matrix between the Computer Aided Design (CAD) coordinate system and the marker coordinate system to achieve the global optimal AR registration visual effect. [PERSON] et al. [31] developed an AR marker-tracking method that is applicable for actual casting processes and applied the method to conduct contactless measurement of the crucible motion during actual aluminum alloy casting processes. However, the marker-based tracking method needs to preset the marker pattern in the real scene, it is not conducive to the user's operation, and when the viewing angle leaves the marker, the tracking registration cannot continue. Therefore, the tracking registration method based on SLAM has gradually become the mainstream research and development direction. In the Parallel Tracking and Mapping (PTAM) algorithm proposed by [PERSON] et al. [32], a classic simultaneous localization and mapping algorithm based on monocular feature points is proposed. By adopting the SLAM technology, the moving object in an unknown environment can acquire the characteristic information of the real-world environment via sensors (e.g., a camera) and construct a map consistent with this environment during movement. SLAM technology was first used in the field of robotics, in recent years, it has been widely used in the field of augmented reality, especially industrial augmented reality. [PERSON] et al. [33] proposed a SLAM-based marker-less mobile AR tracking registration method that can handle virtual image instability caused by fast and irregular camera motion. [PERSON] et al. [34] developed a vSLAM-based AR layout detection system. While the inspectors scan the fab floor using smart-phones, a discrepancy check is automatically made using the floor map generated through vSLAM. [PERSON] et al. [35] proposed a map recovery and fusion strategy based on vision-inertial simultaneous localization and mapping. This strategy is used to solve the problem of map restoration and fusion of collaborative AR systems. However, most of the current SLAM-based industrial AR applications are oriented to specific environments and only suitable for small-scale positioning. When the system is in a large-scale and complex scene, the tracking of the system is easy to lose or drift. In addition, most industrial AR applications cannot save the constructed virtual information, while in actual industrial AR development, such as factory layout planning and mechanical parts assembly, it is often necessary to save the planned virtual scenes or part assembly animations. The QDARV development tool proposed in this paper can quickly create marker-based and SLAM-based industrial AR applications. This tool combines ORB_SLAM2 with YOLOv5 [36] and uses objects as landmarks to assist system positioning.