Designing Probabilistic State Estimators for Autonomous Robot Control (bibtex)

by Thorsten Schmitt and Michael Beetz

Abstract:

This paper sketches and discusses design options for complex probabilistic state estimators and investigates their interactions and their impact on performance. We consider, as an example, the estimation of game states in autonomous robot soccer. We show that many factors other than the choice of algorithms determine the performance of the estimation systems. We propose empirical investigations and learning as necessary tools for the development of successful state estimation systems.

Reference:

Thorsten Schmitt and Michael Beetz, "Designing Probabilistic State Estimators for Autonomous Robot Control", In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2003.

Bibtex Entry:

@InProceedings{Sch03Des,
  author    = "Thorsten Schmitt and Michael Beetz",
  title     = "{Designing Probabilistic State Estimators for Autonomous Robot Control}",
  booktitle = "IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS)",
  year      = "2003",
  bib2html_pubtype  = {Refereed Conference Paper},
  bib2html_rescat   = {RoboCup, State Estimation},
  bib2html_groups   = {AGILO},
  bib2html_funding  = {AGILO},
  bib2html_keywords = {Robot, State Estimation},
  abstract = {This paper sketches and discusses design options for complex probabilistic state estimators and
              investigates their interactions and their impact on performance. We consider, as an example, the
              estimation of game states in autonomous robot soccer. We show that many factors other than the
              choice of algorithms determine the performance of the estimation systems. We propose empirical
              investigations and learning as necessary tools for the development of successful state estimation
              systems.}
}