BibTeX entry

@PHDTHESIS{201311Erkan_Bostanci,
  AUTHOR={G. Erkan Bostanci},
  TITLE={User Tracking Methods for Augmented Reality Applications
    in Cultural Heritage},
  SCHOOL={University of Essex},
  MONTH=Nov,
  YEAR=2013,
  URL={http://www.bmva.org/theses/2013/2013-bostanci.pdf},
}

Abstract

Augmented Reality provides an entertaining means for displaying 3D reconstructions of ancient buildings in situ for cultural heritage. Finding the pose, position and orientation, of the user is crucial for such applications since this information will be used to define the viewpoint that will be used for rendering the models. Images acquired from a camera can be used as the background for such augmentations. To make the most out of this available information, these images can also be utilized to find a pose estimate.

This thesis presents contributions for vision-based methods for estimating the pose of the user in both indoor and outdoor environments. First an evaluation of different feature detectors is presented, making use of spatial statistics to analyse the distribution of the features across the image, a property that is shown to affect the accuracy of the homography calculated from these features.

An analysis of various filtering methods used for tracking was performed and an implementation of a SLAM system is presented. Due to several problems faced with this implementation, there is insufficient tracking accuracy due to linearity problems. An alternative, keyframe-based tracking algorithm is presented.

Continuing with vision-based approaches, Kinect sensor was also used to find the pose of a user for in situ augmentations making use of the natural features in the environment. Skeleton-tracking was also found to be beneficial for such applications.

The thesis then 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 multiple models 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.

Finally, several AR applications are presented that make use of these methods. The first one is for in situ augmentation for displaying historical columns and augmenting users, the second is a virtual visit to an ancient building and the third is a game which can also be played inside the augmentation of the building in the second application.