A Balanced Approach to 3D Tracking from Image Streams
Raghav Subbarao(1,2), Peter Meer(1) and Yakup Genc(2)
(1)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
(2)Real Time Vision and Modeling Group
Siemens Corporate Research
Princeton, NJ
Estimation of camera pose is an integral part of augmented reality
systems. Vision-based methods offer a flexible and accurate method
for this estimation. Current vision based methods rely on markers
to reduce the computation and increase robustness of the pose
estimation. However, this limits the algorithm's applicability
while being expensive since the markers also require maintenance.
Alternatively, reconstructed scene features can be used for pose
estimation but this can lead to a loss of accuracy. To avoid this
we propose a two-stage balanced tracking method which does not
require any visual markers in the scene. The first stage of our
method is based on the sequential recovery of structure from
motion which allows the system to learn the scene from a few
frames in which the markers are visible. In the next stage, the
learned features are used for camera tracking. The system ensures
greater accuracy and reduces error drift due to its use of the
HEIV estimator which is provably unbiased to the first degree. We
also make use of a novel method for the detection and removal of
outliers which are unavoidable in such systems. The experiments
show the superiority of our method when compared to a nonlinear
method based on Levenberg-Marquardt minimization.
4th IEEE and ACM International Symposium on Mixed and Augmented Reality,
Vienna, Austria, October 2005, 70--78.
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