Title:
Probabilistic Structure Matching for Visual SLAM with a Multi-Camera Rig
Probabilistic Structure Matching for Visual SLAM with a Multi-Camera Rig
Authors
Kaess, Michael
Dellaert, Frank
Dellaert, Frank
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Abstract
We propose to use a multi-camera rig for simultaneous localization and mapping
(SLAM), providing flexibility in sensor placement on mobile robot platforms while
exploiting the stronger localization constraints provided by omni-directional sensors.
In this context, we present a novel probabilistic approach to data association, that
takes into account that features can also move between cameras under robot motion.
Our approach circumvents the combinatorial data association problem by using an
incremental expectation maximization algorithm. In the expectation step we determine
a distribution over correspondences by sampling. In the maximization step, we
find optimal parameters of a density over the robot motion and environment structure.
By summarizing the sampling results in so-called virtual measurements, the
resulting optimization simplifies to the equivalent optimization problem for known
correspondences. We present results for simulated data, as well as for data obtained
by a mobile robot equipped with a multi-camera rig.
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Date Issued
2010-02
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Pre-print