J.-S. Gutmann, W. Burgard, D. Fox, and K. Konolige

An experimental comparison of localization methods

Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'98)



Localization is the process of updating the pose of a robot in an environment, based on sensor readings. In this experimental study, we compare two recent methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and scan matching, which uses Kalman filtering techniques based on matching sensor scans. Both these techniques are dense matching methods, that is, they match dense sets of environment features to an a priori map. To arrive at results for a range of situations, we utilize several different types of environments, and add noise to both the dead-reckoning and the sensors. Analysis shows that, roughly, the scan-matching techniques are more efficient and accurate, but Markov localization is better able to cope with large amounts of noise. These results suggest hybrid methods that are efficient, accurate and robust to noise.


Full paper [.ps.gz] (1363 kb, 8 pages)


  AUTHOR            = {Gutmann, J.-S. and Burgard, W. and Fox, D. and Konolige, K.},
  TITLE                  = {An Experimental Comparison of Localization Methods},
  BOOKTITLE     = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and Systems},
  YEAR                   = {1998}

[Back to my research]
[Back to my list of publications]