Localization and tracking of stationary users for augmented reality

In augmented reality applications it is essential to know the position and orientation of the user to correctly register virtual 3D content in the user's field of view. For this purpose, visual tracking through simultaneous localization and mapping (SLAM) is often used. However, when applied to the commonly occurring situation where the users are mostly stationary, many methods presented in previous research have two key limitations. First, SLAM techniques alone do not address the problem of global localization with respect to prior models of the environment. Global localization is essential in many applications where multiple users are expected to track within a shared space, such as spectators at a sporting event. Secondly, these methods often assume significant translational movement to accurately reconstruct and track from a local model of the environment, causing challenges for many stationary applications. In this paper, we extend recent research on Spherical Localization and Tracking to support relocalization after tracking failure, as well as global localization in large shared environments, and optimize the method for operation on mobile hardware. We also evaluate various state-of-the-art localization approaches, the robustness of our visual tracking method, and demonstrate the effectiveness of our system in real-life scenarios.

Automated summary

In augmented reality applications, knowing the user's position and orientation is crucial. However, existing methods for visual tracking through SLAM have limitations in global localization and reconstruction for stationary applications. In this paper, we propose an extended approach for spherical localization and tracking, addressing relocalization after tracking failure, global localization in large shared environments, and optimization for mobile hardware. Our system is evaluated with state-of-the-art localization methods and demonstrates effectiveness in real-life scenarios.