Accurate and Robust Ego-Motion Estimation using Expectation Maximization
conference paper
A novel robust visual-odometry technique, called
EM-SE(3) is presented and compared against using the random
sample consensus (RANSAC) for ego-motion estimation. In
this contribution, stereo-vision is used to generate a number
of minimal-set motion hypothesis. By using EM-SE(3), which
involves expectation maximization on a local linearization of
the rigid-body motion group SE(3), a distinction can be made
between inlier and outlier motion hypothesis. At the same time
a robust mean motion as well as its associated uncertainty can
be computed on the selected inlier motion hypothesis. The datasets
used for evaluation consist of synthetic and large real-world
urban scenes, including several independently moving objects.
Using these data-sets, it will be shown that EM-SE(3) is both
more accurate and more efficient than RANSAC
EM-SE(3) is presented and compared against using the random
sample consensus (RANSAC) for ego-motion estimation. In
this contribution, stereo-vision is used to generate a number
of minimal-set motion hypothesis. By using EM-SE(3), which
involves expectation maximization on a local linearization of
the rigid-body motion group SE(3), a distinction can be made
between inlier and outlier motion hypothesis. At the same time
a robust mean motion as well as its associated uncertainty can
be computed on the selected inlier motion hypothesis. The datasets
used for evaluation consist of synthetic and large real-world
urban scenes, including several independently moving objects.
Using these data-sets, it will be shown that EM-SE(3) is both
more accurate and more efficient than RANSAC
TNO Identifier
242071
Publisher
IEEE
Source title
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems - IROS 2008, Sept, 22-26, 2008, Nice, France,
Place of publication
Piscataway, NJ
Pages
3914-3920
Files
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