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

• U. Frese ^[1]
  1. [1] University of Bremen

     University of Bremen

     Kreisfreie Stadt Bremen, Alemania

     
• Localización: Autonomous robots, ISSN 0929-5593, Vol. 21, Nº 2, 2006, págs. 103-122
• Idioma: inglés
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• Resumen

  • 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.