  A tracking system that will be used for Augmented Reality (AR) applications
has two main requirements: accuracy and frame rate. The first requirement is
related to the performance of the pose estimation algorithm and how accurately
the tracking system can find the position and orientation of the user in the
environment. Accuracy problems of current tracking devices, considering that
they are low-cost devices, cause static errors during this motion estimation
process. The second requirement is related to dynamic errors (the end-to-end
system delay; occurring because of the delay in estimating the motion of the
user and displaying images based on this estimate. This paper investigates
combining the vision-based estimates with measurements from other sensors, GPS
and IMU, in order to improve the tracking accuracy in outdoor environments. The
idea of using Fuzzy Adaptive Multiple Models (FAMM) was investigated using a
novel fuzzy rule-based approach to decide on the model that results in improved
accuracy and faster convergence for the fusion filter. Results show that the
developed tracking system is more accurate than a conventional GPS-IMU fusion
approach due to additional estimates from a camera and fuzzy motion models. The
paper also presents an application in cultural heritage context.
