GVU Technical Report Number:
GIT-GVU-91-12
Title:
Constructing a Global Representation from Multiple Stereo Images
Authors:
Warren Gardner
Junichi Hoshino
Abstract:
The problem of using passive vision to construct 3-D descriptions of the
environment is commonly encountered in computer vision and robotics.
This paper focuses on the problem of merging multiple stereo images into
a single global representation. The problem of how to fuse multiple data
measurements is one which arises frequently in the field of robotics and
computer vision. This type of problem is encountered while attempting to
interpret data from a single source, but becomes more apparent when
multiple sources or sensors are involved. This paper will focus on a
tool, the Extended Kalman Filter (EKF), which is capable of fusing
multiple erroneous measurements while reducing the uncertainty associated
with these measurements.
Section 2 introduces both the Kalman Filter and the EKF. The Kalman
Filter is a linear, recursive, mean-square error filter. The EKF is
based on the Kalman Filter, but is used for estimating nonlinear
systems. Section 3 applies the EKF methodology to the problem of
estimating the displacement between a pair of stereo images. A method of
matching lines between images is discussed within the EKF framework.
Section 4 introduces the error models used by the EKF. In Section 5 the
problem of merging stereo images given an accurate estimate of their
displacement is discussed. Section 6 examines the problem of
divergence. The results of an implementation of the above systems is
presented in Section 7. Section 8 contains recommendations for future
research in the area of 3-D reconstruction with the EKF.
Keywords:
3-D imaging, Extended Kalman Filter, Kalman Filter, stereo images
You can access this technical report via:
PDF
Postscript
|
