An analysis of orientation prediction and filtering methods for VR/AR

To enable a user to perform virtual reality tasks as efficiently as possible, reducing tracking inaccuracies from noise and latency is crucial. Much work has been done to improve tracking performance by using predictive filtering methods. However, it is unclear what the benefits of each of these methods are in practice, which parameters influence their performance, and what the extent of this influence is. We present an analysis of various orientation prediction and filtering methods using various hand tasks and synthetic signals, and evaluate their performance in relation to each other. We identify critical parameters and analyse their influence on accuracy. Our results show that for the tested datasets, the use of an EKF is sufficient for orientation prediction in VR/AR.