# Mapping Server Collaboration Architecture Design with OpenVSLAM for Mobile Devices
## 1 Introduction  
Simultaneous localization and mapping (SLAM) technology belongs to the information communication technology (ICT) application field, and it is a technology in which a mobile device creates an environment map and calculates a location within the environment using the generated map at the same time. The field of SLAM is growing remarkably due to advances in computer vision and robotics, and there are various types, such as extended Kalman filter (EKF) SLAM [1], rapid SLAM [2], and graph-based SLAM [3, 4]. Among the various SLAM technologies, visual SLAM based on machine learning and computer vision, which uses camera image data to track feature points in images and create maps, is considered to be the next-generation technology that supports industries such as automobiles, drones, and mixed reality. In particular, thanks to the spread of remote and non-contact culture caused by COVID-19, augmented reality (AR) and virtual reality (VR) application technologies, such as digital twins, are attracting attention. In addition, as the demand for indoor applications where maps do not exist and the demand for drones in military and commercial applications increase, the need for SLAM technology development is also growing. A SLAM technology that uses various sensors that can replace markers to search for information, estimate one's location in an arbitrary space, and generate a spatial map has emerged [5]. The markerless AR system recognizes an object by detecting feature points of an object or image without prior knowledge of the environment, such as a wall or intersection, and, through this, 3D contents can be placed at a specific location. The markerless AR technology not only improves the accuracy of image analysis with thedevelopment of SLAM technology, which is a simultaneous localization and mapping technology, but also detects the surroundings and synchronizes the current location of the targets through various sensor data [6]. In AR applications, the real-time camera pose and the distance between the camera and the object are more important, and the accuracy of SLAM system mapping and global positioning is relatively low [7]. Creating a map of SLAM requires solutions to problems such as localization. Mapping and path planning are required, and robust path planning depends on successful localization and mapping. Both problems can be overcome with SLAM techniques. Since sequential sensor information is required for SLAM, eliminating these sensor noises is crucial for the next measurement and prediction [8]. Visual SLAM requires camera-based visual SLAM technology or SLAM technology using initial measurement unit (IMU) sensors or LiDAR sensors because UAVs used in indoor spaces cannot be positioned using GPS [9]. SLAM systems can be divided into direct SLAM, which uses every pixel of the camera frame, and indirect SLAM, which uses a series of key frames and feature points. Direct SLAM has a slow processing speed, but it is possible to model the surrounding environment closely and has excellent performance, even in a featureless environment. On the other hand, indirect SLAM a has fast processing speed because it estimates the camera location using certain pixels in the image and creates a 3D map [10]. In the field of AR, ORB-SLAM [11; 12; 13] and OpenVSLAM [14], which are indirect SLAM systems that can be mainly used for robots or autonomous driving, are used. However, since these SLAM libraries are mainly developed for robots related to autonomous driving, although they are open-source software, they are rarely used for the purpose of realizing AR in mobile devices or augmented reality headsets. It is especially difficult to find open SLAM libraries for mobile devices. Therefore, AR contents for mobile devices are mostly developed using solutions provided with software development kits (SDK) by companies such as Google's ARCore, Apple's ARKit, and Maxst's SensorFusionSLAM.