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SLAM algorithms differ depending on the map type. State of
the art for metric maps are probabilistic methods, where the
robot has probabilistic motion and uncertain perception
models. By integrating these two distributions with a Bayes
filter, e.g., Kalman or particle filter, it is possible to
localize the robot. Mapping is often regarded as an extension to
this estimation problem. Beside the robot pose, positions of
landmarks are estimated. Closed loops, i.e., a second encounter
of a previously visited area of the environment, play a special
role here. Once detected, they enable the algorithms to bound the
error by deforming the already mapped area such that a
topologically consistent model is created. However, there is no
guarantee for a correct model. Several strategies exist for
solving SLAM. Thrun reviews in [27] existing
techniques, i.e., maximum likelihood estimation
[9], expectation maximization
[8,28], extended Kalman filter
[6] or (sparse extended) information filter
[30]. In addition to these methods, FastSLAM
[29] that approximates the posterior probabilities,
i.e., robot poses, by particles, and the method of Lu and Milios on
the basis of IDC scan matching [15] exist.
In principle, these probabilistic methods are extendable to
6D. However, no reliable feature extraction nor a strategy for
reducing the computational costs of multi hypothesis tracking,
e.g., FastSLAM, that grows exponentially with the degrees of
freedom, has been published to our knowledge.

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2006-03-16