Treemap: An O(log n) algorithm for indoor simultaneous localization and mapping

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 landma...

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Bibliographic Details
Published inAutonomous robots Vol. 21; no. 2; pp. 103 - 122
Main Author Frese, Udo
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Nature B.V 01.09.2006
Subjects
Algorithms
Computer simulation
Computing time
Landmarks
Least squares
Simultaneous localization and mapping
Studies
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Summary: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.
Bibliography:ObjectType-Article-2
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ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-006-9043-2