Adapting proposal distributions for accurate, efficient mobile robot localization

Patrick Beeson, Aniket Murarka, and Benjamin Kuipers. Adapting proposal distributions for accurate, efficient mobile robot localization. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 49–55, Orlando, Florida, May 2006.

Abstract

When performing probabilistic localization using a particle filter, a robot must have a good proposal distribution in which to distribute its particles. Once weighted by their normalized likelihood scores, these particles estimate a posterior distribution of the robot's possible poses. This paper 1) introduces a new action model (the system of equations used to determine the proposal distribution at each time step) that can run on any differential drive robot, even from log file data, 2) investigates the results of different algorithms that modify the proposal distribution at each time step in order to obtain more accurate localization, 3) investigates the results of incrementally adapting the action model parameters based on recent localization results in order to obtain efficient proposal distributions that better approximate the true posteriors. The results show that by adapting the action model over time and, when necessary, modifying the resulting proposal distributions at each time step, localization improves---the maximum likelihood score increases and, when possible, the percentage of wasted particles decreases.

Additional Information

Talk slides

BibTeX

@InProceedings{Beeson-icra-06,
  author =       {Patrick Beeson and Aniket Murarka and Benjamin Kuipers},
  title =        {Adapting proposal distributions for accurate, efficient
                  mobile robot localization},
  booktitle =    {Proceedings of the IEEE International Conference on Robotics
                  and Automation (ICRA)},
  year =         2006,
  address =      {Orlando, Florida},
  month =        {May},
  pages =        {49--55},
  abstract =     {When performing probabilistic localization using a particle
                  filter, a robot must have a good proposal distribution in
                  which to distribute its particles. Once weighted by their
                  normalized likelihood scores, these particles estimate a
                  posterior distribution of the robot's possible poses. This
                  paper 1) introduces a new action model (the system of
                  equations used to determine the proposal distribution at
                  each time step) that can run on any differential drive
                  robot, even from log file data, 2) investigates the results
                  of different algorithms that modify the proposal
                  distribution at each time step in order to obtain more
                  accurate localization, 3) investigates the results of
                  incrementally adapting the action model parameters based on
                  recent localization results in order to obtain efficient
                  proposal distributions that better approximate the true
                  posteriors. The results show that by adapting the action
                  model over time and, when necessary, modifying the resulting
                  proposal distributions at each time step, localization
                  improves---the maximum likelihood score increases and, when
                  possible, the percentage of wasted particles decreases.},
  bib2html_pubtype ={Refereed Conference},
  bib2html_rescat ={Simultaneous Localization and Mapping (SLAM)},
  bib2html_extra_info ={<a
                  href="http://personal.traclabs.com/~pbeeson/talks/Beeson-icra-06_talk.pdf">
                  Talk slides</a>},
}

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