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

摘要

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 within this thesis was to extend the excellent static orientation sensing abilities of accelerometers to a dynamic case by utilising a model of head motion. Head motion is modelled by an inverted pendulum, initially for one degree of rotational freedom, but later this is extended to a more general two dimensional case by including a translational freedom of the centre of rotation. However, the inverted pendulum model consists of an unstable coupled set of differential equations which cannot be solved by conventional solution approaches.A unique method is developed which consists of a highly accurate approximated analytical solution to the full non linear tangential ODE. The major advantage of the analytical solution is that it allows a separation of the unstable transient part of the solution from the stable solution. The analytical solution is written directly in terms of the unknown initial conditions. Optimal initial conditions are found that remove the unstable transient part completely by utilising the independent radial ODE. Thus, leaving the required orientation.The methods are validated experimentally with data collected using accelerometers and a physical inverted pendulum apparatus. A range of tests were performed demonstrating the stability of the methods and solution over time and the robust performance to increasing signal frequency, over the range expected for head motion. The key advantage of this accelerometer model-based method is that the orientation remains registered to the gravitational vector, providing a drift free 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.