Abstract
This paper presents a very efficient SLAM algorithm that works by hierarchically dividing the 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. For keeping the matrices small only those landmarks are represented being 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 global update is necessary requiring only O(k 3 log n) computation time for n overall landmarks. 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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Frese, U. (2005). Treemap: An O(log n) Algorithm for Simultaneous Localization and Mapping. In: Freksa, C., Knauff, M., Krieg-Brückner, B., Nebel, B., Barkowsky, T. (eds) Spatial Cognition IV. Reasoning, Action, Interaction. Spatial Cognition 2004. Lecture Notes in Computer Science(), vol 3343. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32255-9_25
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DOI: https://doi.org/10.1007/978-3-540-32255-9_25
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