Towards Lazy Data Association in SLAM

  • Dirk Hähnel
  • Sebastian Thrun
  • Ben Wegbreit
  • Wolfram Burgard
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 15)

Abstract

We present a lazy data association algorithm for the simultaneous localization and mapping (SLAM) problem. Our approach uses a tree-structured Bayesian representation of map posteriors that makes it possible to revise data association decisions arbitrarily far into the past. We describe a criterion for detecting and repairing poor data association decisions. This technique makes it possible to acquire maps of large-scale environments with many loops, with a minimum of computational overhead for the management of multiple data association hypotheses. A empirical comparison with the popular FastSLAM algorithm shows the advantage of lazy over proactive data association.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dirk Hähnel
    • 1
  • Sebastian Thrun
    • 2
  • Ben Wegbreit
    • 2
  • Wolfram Burgard
    • 1
  1. 1.Department of Computer ScienceUniversity of FreiburgGermany
  2. 2.Department of Computer ScienceStanford UniversityStanfordUSA

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