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Monte Carlo Localization in Outdoor Terrains Using Multi-Level Surface Maps

  • Rainer Kümmerle
  • Rudolph Triebel
  • Patrick Pfaff
  • Wolfram Burgard
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 42)

Summary

In this paper we consider the problem of mobile robot localization with range sensors in outdoor environments. Our approach applies a particle filter to estimate the full six-dimensional state of the robot. To represent the environment we utilize multi-level surface maps which allow the robot to represent vertical structures and multiple levels in the environment. We describe probabilistic motion and sensor models to calculate the proposal distribution and to evaluate the likelihood of observations. Experimental results obtained with a mobile robot in an outdoor environment indicate that our approach can be used to robustly and accurately localize an outdoor vehicle. The experiments also demonstrate that multi-level surface maps lead to a significantly better localization performance than standard elevation maps.

Keywords

Mobile Robot Motion Vector Outdoor Environment Sensor Model Global Localization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rainer Kümmerle
    • 1
  • Rudolph Triebel
    • 1
    • 2
  • Patrick Pfaff
    • 1
  • Wolfram Burgard
    • 1
  1. 1.Department of Computer ScienceUniversity of FreiburgGermany
  2. 2.Autonomous Systems LabSwiss Federal Institute of TechnologyZurichSwitzerland

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