A Multiple Model Approach to Track Head Orientation With Delta Quaternions

Area of Science:

• Computer Science
• Human-Computer Interaction
• Virtual Reality

Background:

• Virtual and augmented reality (VR/AR) rely on immersive experiences, which are disrupted by display lag.
• Display lag occurs when there's a delay between user head motion and visual display updates.
• Predicting head orientation is crucial for minimizing display lag and maintaining user presence.

Purpose of the Study:

• To propose a novel head orientation prediction technique for VR/AR systems.
• To improve the accuracy of predicting future head orientation to counteract display lag.
• To enhance the sense of presence and immersion in VR/AR environments.

Main Methods:

• A multiple delta quaternion (DQ) extended Kalman filter was developed to track angular head velocity and acceleration.
• The method utilizes quaternion orientation measurements, independent of the specific orientation sensor.
• A new prediction algorithm estimates future head orientation based on current measurements and predicted changes.

Main Results:

• The proposed method effectively tracks angular head velocity and acceleration.
• Extensive experiments demonstrated improved head orientation prediction accuracy compared to single filter DQ prediction.
• The technique successfully minimizes the impact of display lag on user experience.

Conclusions:

• The novel multiple DQ extended Kalman filter provides a robust solution for head orientation prediction in VR/AR.
• Accurate head orientation prediction is key to mitigating display lag and enhancing immersion.
• This technique offers a device-independent approach for improving VR/AR performance.