Comparison between particle filter approach and Kalman filter-based technique for head tracking in augmented reality systems

Abstract

The major problem with augmented reality (AR) systems using see-through head mounted displays (HMD's) is the end-to-end system delay (or latency). This delay exists because the head tracker, scene generator, and communication links require time to perform their tasks, causing a lag between the measurement of head location and the display of the corresponding virtual objects inside the HMD. One way to eliminate or reduce the latency is to predict future head locations. We propose to use optimal Bayesian algorithms for non-linear/non-Gaussian tracking problems, with a focus on particle filters to predict head motion. Particle filters are sequential Monte Carlo methods based upon point mass (or 'particle') representation of probability densities, which can applied to any state space model, and which generalize the traditional Kalman filtering methods. A SIR particle filter is discussed and compared with the standard EKF through an illustrative example.