Handling Error in Ubiquitous Tracking Setups
Student: Daniel Pustka
Supervisors: Martin Bauer,
Asa MacWilliams,
Martin Wagner
Professor: Prof. Gudrun Klinker
Submission date: August 15th, 2004
Part of the
UbiTrack Project?
Abstract
Augmented reality is a technology that aims at combining real and
virtual reality by integrating virtual objects into the user's view of
the real world. In order to place these augmentations at the right
locations, different tracking technologies are employed for finding
out where the user is and in what direction he is looking. This
requires high accuracy, high update rates and low delay to give a
convincing impression.
Ubiquitous tracking is a new research project focused at dynamically
integrating user-worn and stationary tracking systems by combining
the concepts of augmented reality and ubiquitous computing in order to
enable mobile wide-area augmented reality applications.
When many different and previously unknown tracking technologies are to
be combined dynamically at runtime, statistics about the sensor
accuracy are necessary and all transformations of measurements require
updating the associated error descriptions.
In order to allow automatic conversions between different coordinate
systems, the product of multiple tracker measurements can be computed.
In heterogeneous tracking setups however, measurements by different
sensors generally are not made simultaneously and therefore require
pre-processing before such a combination is possible.
This thesis proposes a mathematical model as well as a software
architecture based on the dwarf framework for describing sensor
errors in position and orientation in Ubitrack systems. Using a
Gaussian error model, errors are propagated over multiple chained
coordinate system transformations. A Kalman filter-based approach with
separate motion models is described for dynamic sensor fusion,
measurement simultaneity, prediction, as well as the estimation of
static transformations.
Thesis Download
Proposal (old stuff)