Robust Dynamic Orientation Sensing Using Accelerometers: Model-based Methods for Head Tracking in AR

Robust Dynamic Orientation Sensing Using Accelerometers: Model-based Methods for Head Tracking in AR

Abstract

Augmented reality (AR) systems that use head mounted displays to overlay synthetic imagery on the user's view of the real world require accurate viewpoint tracking for quality applications. However, achieving accurate registration is one of the most significant unsolved problems within AR systems, particularly during dynamic motions in unprepared environments. As a result, registration error is a major issue hindering the more widespread growth of AR applications.
The main objective for this thesis was to improve dynamic orientation tracking of the head using low cost inertial sensors. The approach taken was to extend the excellent static orientation sensing abilities of accelerometers to a dynamic case by utilising a model of head motion. The inverted pendulum model utilised consists of an unstable coupled set of differential equations which cannot be solved by conventional solution approaches. A unique method is presented and validated experimentally with data collected using accelerometers and a physical inverted pendulum apparatus.
The key advantage of this accelerometer model-based method is that the orientation remains registered to the gravitational vector, providing a drift free orientation solution that outperforms existing, state of the art, gyroscope based methods. This proof of concept uses low-cost accelerometer sensors to show significant potential to improve head tracking in dynamic AR environments, such as outdoors.