Abstract
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(k2) 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(k3 log n) computation time for n landmarks. A global least square estimate needs O(kn) computation time with a very small constant (12.37 ms for n = 11300).
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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This article is based on the authors studies at the German Aerospace Center.
Udo Frese was born in Minden, Germany in 1972. He received the Diploma degree in computer science from the University of Paderborn in 1997. From 1998 to 2003 he was a Ph.D. student at the German Aerospace Center in Oberpfaffenhofen. In 2004 he received his Ph.D. degree from University of Erlangen-Nürnberg and joined SFB/TR 8 Spatial Cognition at University of Bremen. He works on mobile robotics, SLAM and computer vision.
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Frese, U. Treemap: An O(log n) algorithm for indoor simultaneous localization and mapping. Auton Robot 21, 103–122 (2006). https://doi.org/10.1007/s10514-006-9043-2
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DOI: https://doi.org/10.1007/s10514-006-9043-2