by T Schmitt and M 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:
Designing Probabilistic State Estimators for Autonomous Robot Control (T Schmitt and M Beetz), In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2003.
Bibtex Entry:
@inproceedings{schmitt_designing_2003,
author = {T Schmitt and M Beetz},
title = {Designing Probabilistic State Estimators for Autonomous Robot Control},
booktitle = {{IEEE/RSJ} Intl. Conf. on Intelligent Robots and Systems ({IROS)}},
year = {2003},
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.},
}