| Abstract / Kurzbeschreibung: |
This article presents a very efficient SLAM algorithm that works by hierarchically dividing a map into local regions and subregions. At each level of the hierarchy each region stores a matrix representing some of the landmarks contained in this
region. To keep those matrices small, only those landmarks are
represented that are observable from outside the region.
A measurement is integrated into a local subregion using
$O(k^2)$ computation time for $k$ landmarks in a subregion. When the robot moves to a different subregion a full least-square estimate for that region is computed in only $O(k^3 log n)$ computation time for $n$ landmarks. A global least square estimate needs $O(kn)$ computation time with a very small constant ($simexHugeLastT$ for $n=simexHugeN$). Furthermore, the proposed hierarchy allows the rotation of individual regions to reduce linearization errors.
The algorithm is evaluated for map quality, storage space and
computation time using simulated and real experiments in an
office environment.
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