# Mapping Server Collaboration Architecture Design with OpenVSLAM for Mobile Devices
## 5 Conclusions  
In this paper, to reduce unnecessary self-research by researchers and contribute to the rapid growth of the SLAM field, an OpenVSLAM-based AR system was designed and implemented in a mobile environment for the purpose of open-source software, and its function and performance were evaluated not only in an actual indoor space but also using the EuRoC datasets. To implement this system, image data and IMU data were collected using the camera on a mobile device. In addition, SLAM plugins were created to use SLAM functions, including the tracking, regional and global map generation, loop closure detection, and localization functions in Unity-based mobile applications.  
By building a mapping server based on the FastAPI server, implementing SLAM functions that require a lot of computation, such as mapping and optimization, on the server, and transmitting it to the mobile app, a method to reduce the overhead of SLAM operation in a mobile environment was proposed. This allows users to continuously track their location along with a map generated in advance for the indoor and outdoor environment they are looking at and to build a service that can provide AR contents based on a more accurate location. When the accuracy was evaluated by comparing the RMSE of the trajectory based on the monocular camera with the ground-truth data of the EuRoC dataset, the average of 2.017 was shown, and it was conformed that materialization of the main object is possible through the visualization of the point cloud for the actual indoor space.  
Through the system in this paper, the cost of constructing an AR system for a wide range of environments can be reduced with only a mobile device and a laptop without using a separate camera or other expensive equipment. We hope that this will serve as a basis for applying SLAM technology to various application fields, such as safe and reliable autonomous driving technology for drones and vehicles, multi-access AR system implementation technology, and indoor space navigation.  
In the future, based on this system, we plan to use the LiDAR sensor by changing the Android-based data collector to an iOS-based data collector. It is expected to implement a lighter system by providing a localization function to Android-based mobile devices without depth sensors after taking a map of the surrounding environment using an iOS-based mobile device.  
Conceptualization, J.S. and J.K.; methodology, J.S. and J.K.; software, J.S.; validation, J.S. and J.K.; formal analysis, J.S.; investigation, J.S.; resources, J.S.; data curation, J.S. and J.K.; writing--original draft preparation, J.S. and J.K.; writing--review and editing, J.K.; visualization, J.S.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
Figure 11: SLAM visualization results by RTAB-Map application.  
**Funding:** This work was carried out with the support of Green Convergence Professional Manpower Training Program of the Korea Environmental Industry and Technology Institute funded by the Ministry of Environment.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.