# Mapping Server Collaboration Architecture Design with OpenVSLAM for Mobile Devices
## 2 Related Works
### Markerless-Based AR  
In the early AR systems, markers were used to connect the real world and the virtual world. The marker-based AR system recognizes a marker from an image input by a camera and represents it by overlaying 2D or 3D contents related to the marker on an image output to a display device. Simple patterns, such as QR codes or logos, are mainly used for the shape of the marker, and when the marker is recognized, the location or direction of the marker is also calculated, allowing digital 3D contents to be placed in an augmented reality application. However, the marker-based AR requires a prior appointment for a marker, and it has technical limitations, such as making tracking impossible when the camera leaves the marker. In the marker-based AR system, the three steps of marker recognition, reasoning, and representation can be configured as shown in [15]. On the other hand, the markerless-based AR system provides contents by recognizing a specific object or space instead of a pre-arranged marker, so it requires that the marker recognition, reasoning, and representation must be made on the mobile devices themselves, unlike the marker-based AR system.  
In order to overcome the limitations of marker-based AR technology, a SLAM technology has emerged 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. 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 the development 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. Representative SLAM-based markerless AR development tools include Google's ARCore, Apple's ARKit and RealityKit2, and PTC's Vuforia.  
Google's ARCore provides SDKs that support multiple platforms, including mobile platforms, such as Android and iOS, to build an AR system, allowing mobile devices to detect their surroundings and interact with information about their surroundings. ARCore uses three main functions to integrate virtual contents with the real world viewed through a mobile device's camera: motion tracking, environmental understanding, and lighting estimation. Basically, ARCore performs two tasks: it tracks the location of moving mobile devices and builds its own environment. ARCore's tracking technology uses a mobile device's camera to identify specific points and track how those points move over time, combining image data and IMU data from the mobile device to track its location and direction as the mobile device moves through space. In addition to identifying feature points, flat surfaces can be detected, and the average illumination of the surrounding area can also be estimated, making it possible to build map data for the surrounding environment on its own. These functions allow you to build a totally new AR system or improve existing applications [16].  
Apple's ARKit is a software framework for making AR applications, which was unveiled at WWDC17 and which enables AR technology to be implemented and used in Apple products, such as the iPhone, iPad, and Mac. Recently, Apple released RealityKit2, which integrates ARKit and SWIFT API, and these are being used in the 'Measure' application, an app for distance measurement, and the App Store app for providing Apple product information based on AR. In addition, Apple is widely applying more precise AR technology by additionally using depth information of pixels depending on the changesin the surrounding environment, such as the spatial audio function added to the AirPod Pro and the LiDAR sensor installed in iPhone 12 Pro and iPad Pro [17].