Robust Localization Methods for Scalable Shared AR Environments

This paper proposes two novel localization methods for Shared Augmented Reality (Shared AR) addressing limitations of conventional approaches. Traditional methods struggle when image features cannot be reliably detected or when users are located far apart, making spatial alignment difficult. To overcome these issues, a Geolocation-Based method and a Depth-Based method tailored for such challenging scenarios are introduced. Additionally, this paper proposes a method to improve geographic accuracy by leveraging a Shared AR network. The experimental results demonstrate that the proposed localization methods maintain practically acceptable error levels and are effective even in scenarios where conventional methods are inapplicable.