When placing a Head-Mounted Display (HMD) of a Virtual Reality (VR) or Augmented Reality (AR) system in the cabin of a moving base simulator to provide a larger field-of-regard, the HMD's inertial sensors will interpret the motion of the moving base simulator as if provided by the user, thus distorting the HMD's cabin-fixed pose estimation.
In order to estimate a proper cabin-fixed HMD pose, this thesis proposes an Unscented Kalman Filter (UKF) that fuses the information from the simulator sensors, as well as the sensors of the VR/AR system, including its visual position sensor and IMU. For this, three sensor configurations are proposed and tested offline deterministically based on real data. Although an unexpected latency in the HMD's visual pose sensor prevented a full validation of the algorithms, both the configuration with an IMU attached to the motion base, and the configuration without an IMU but with second-order additive noise variables added to the UKF, resulted in a proper, tight innovation sequence, indicating accurate system state estimation. The third configuration, one using the novel approach of using setpoints, provided a tight, but out-of-bounds, innovation sequence indicative of high accuracy, but also diminished tuning efficacy. It is also sensitive to stability issues when estimating the motion system's dynamics when no movement is present on the moving base. The unaltered setpoint method is not recommended as a solution. However, a hybrid solution of the setpoint and second-order additive noise variables methods should be investigated in a follow-up study to combine stability with high accuracy. Another recommendation is to test all configurations interactively, i.e. online.