CollabLoc: Collaborative Information Sharing for Real-Time Multiuser Visual Localization System

This paper presents CollabLoc, a novel approach for real-time multi-user visual localization. Typically, localization systems employ a client-server design for locating cameras. In these systems, lightweight simultaneous localization and mapping computations are performed on the client side, while the server handles intensive localization tasks. This approach harnesses the complementary capabilities of the client and server, resulting in accurate, real-time localization results. However, existing architectures primarily operate on a one-to-one client-server structure, limiting their scalability and multi-user capabilities. Therefore, CollabLoc is designed to accommodate multiple clients through collaborative information sharing to considerably reduce computational overhead and enhance overall efficiency and accuracy. We propose a tracking confidence module that evaluates the tracking quality of individual clients and plays a pivotal role in prioritizing client requests by the server-side algorithm. On the server, we utilize fused poses to accelerate image retrieval. Moreover, we enhance the efficiency of optical flow estimation by employing a simplified feature extraction module and leveraging spatial similarities among neighboring clients to improve its performance. Finally, via the Pose Fusion Module, the server can periodically adjust fused poses to mitigate accumulated errors. Experimental results indicate that compared with a baseline method, CollabLoc improves positioning efficiency by nearly twice and achieves higher accuracy in multi-user scenarios.