A Monocular SLAM-based Multi-User Positioning System with Image Occlusion in Augmented Reality

• URL: http://arxiv.org/abs/2411.10940v1
• Date: Sun, 17 Nov 2024 02:39:30 GMT
• Title: A Monocular SLAM-based Multi-User Positioning System with Image Occlusion in Augmented Reality
• Authors: Wei-Hsiang Lien, Benedictus Kent Chandra, Robin Fischer, Ya-Hui Tang, Shiann-Jang Wang, Wei-En Hsu, Li-Chen Fu,
• Abstract summary: We propose a multi-user localization system based on ORB-SLAM2 using monocular RGB images as a development platform based on the Unity 3D game engine. This system not only performs user localization but also places a common virtual object on a planar surface so that every user holds a proper perspective view of the object. The positioning information is passed among every user's AR devices via a central server, based on which the relative position and movement of other users in the space of a specific user are presented.
• Score: 2.8155732302036176
• License:
• Abstract: In recent years, with the rapid development of augmented reality (AR) technology, there is an increasing demand for multi-user collaborative experiences. Unlike for single-user experiences, ensuring the spatial localization of every user and maintaining synchronization and consistency of positioning and orientation across multiple users is a significant challenge. In this paper, we propose a multi-user localization system based on ORB-SLAM2 using monocular RGB images as a development platform based on the Unity 3D game engine. This system not only performs user localization but also places a common virtual object on a planar surface (such as table) in the environment so that every user holds a proper perspective view of the object. These generated virtual objects serve as reference points for multi-user position synchronization. The positioning information is passed among every user's AR devices via a central server, based on which the relative position and movement of other users in the space of a specific user are presented via virtual avatars all with respect to these virtual objects. In addition, we use deep learning techniques to estimate the depth map of an image from a single RGB image to solve occlusion problems in AR applications, making virtual objects appear more natural in AR scenes.

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