Co-Located VR with Hybrid SLAM-based HMD Tracking and Motion Capture Synchronization

2025-09-08

AI Summary

Existing multi-user co-located VR systems suffer from motion drift, high latency, and jitter, compromising long-term spatial consistency and real-time interactive fidelity. This paper proposes a hybrid tracking framework that synergistically integrates low-latency, inside-out SLAM on head-mounted displays (HMDs) for local pose estimation with high-precision, external optical motion capture for global pose correction. The framework supports dynamic realignment to suppress cumulative drift. Through spatiotemporal registration and real-time pose synchronization, it achieves sub-centimeter spatial consistency across multiple users within a shared virtual environment. Experimental results demonstrate that the framework sustains ≥90 Hz rendering frame rates and end-to-end latency under 20 ms, while significantly improving system robustness, scalability, and user wearing comfort. This work provides a reliable technical foundation for large-scale, co-located VR interaction.

Technology Category

• Computer Vision: Motion & Tracking
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Tracking, profiling, and countermeasures against them

Abstract

We introduce a multi-user VR co-location framework that synchronizes users within a shared virtual environment aligned to physical space. Our approach combines a motion capture system with SLAM-based inside-out tracking to deliver smooth, high-framerate, low-latency performance. Previous methods either rely on continuous external tracking, which introduces latency and jitter, or on one-time calibration, which cannot correct drift over time. In contrast, our approach combines the responsiveness of local HMD SLAM tracking with the flexibility to realign to an external source when needed. It also supports real-time pose sharing across devices, ensuring consistent spatial alignment and engagement between users. Our evaluation demonstrates that our framework achieves the spatial accuracy required for natural multi-user interaction while offering improved comfort, scalability, and robustness over existing co-located VR solutions.

Problem

• Synchronizing multiple users in shared VR environments
• Combining SLAM tracking with motion capture systems
• Correcting drift and latency in co-located VR

Innovation

• Combines motion capture with SLAM-based inside-out tracking
• Uses external source realignment to correct tracking drift
• Enables real-time pose sharing across multiple VR devices