Abstract
The simultaneous localization and mapping (SLAM) market is growing rapidly with advances in Machine Learning, Drones, and Augmented Reality (AR) technologies. However, due to the absence of an open source-based SLAM library for developing AR content, most SLAM researchers are required to conduct their own research and development to customize SLAM. In this paper, we propose an open source-based Mobile Markerless AR System by building our own pipeline based on Visual SLAM. To implement the Mobile AR System of this paper, we use ORB-SLAM3 and Unity Engine and experiment with running our system in a real environment and confirming it in the Unity Engine’s Mobile Viewer. Through this experimentation, we can verify that the Unity Engine and the SLAM System are tightly integrated and communicate smoothly. In addition, we expect to accelerate the growth of SLAM technology through this research.
Highlights
Simultaneous localization and mapping (SLAM) technology is gaining popularity in Drones and Augmented Reality (AR) with advancement in 3D Graphics technology and Machine Learning technology
The EuRoC MH03 dataset is a visual inertial data set collected from a Micro Aerial Vehicle (MAV), including stereo images, synchronized
We designed a SLAM-based AR system in a mobile environment with good accessibility for the purpose of open source in order to reduce the burden of researchers’ and to contribute to faster growth in the SLAM field
Summary
Simultaneous localization and mapping (SLAM) technology is gaining popularity in Drones and Augmented Reality (AR) with advancement in 3D Graphics technology and Machine Learning technology. Indirect SLAM systems such as ORB-SLAM [3] and Open-VSLAM [4] are being used in the robot or autonomous driving field As these SLAM libraries are open source projects for general-purpose systems, it is somewhat difficult to implement AR contents on commercial devices such as mobile devices or AR headsets. We design a markerless AR and data pipeline via ORB-SLAM3, which can extract features in real time as it is the fastest among the existing open source SLAM libraries and offers a compromise between the quality and the performance among the modern SLAM systems, to implement a system that supports the development of AR contents on mobile devices or AR headsets through markerless SLAM [5]. 5 describes the usability of ARSLAM contents, and in in aparticular, various open source projects have made itfuture possible to use AR contents onexpected various types computing development plans and effectsoffor this study.platforms
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