Towards effective localization in dynamic environments
Dali Sun, Florian Geißer, Bernhard Nebel
Abstract
Localization in dynamic environments is still a challenging problem in robotics - especially if rapid and large changes occur irregularly. Inspired by SLAM algorithms, our Bayesian approach to this so-called dynamic localization problem divides it into a localization problem and a mapping problem, respectively. To tackle the localization problem we use a particle filter, coupled with a distance filter and a scan matching method, which achieves a more robust localization against dynamic obstacles. For the mapping problem we use an extended sensor model which results in an effective and precise map update effect. We compare our approach against other localization methods and evaluate the impact the map update effect has on the localization in dynamic environments.
BibTeX
@inproceedings{iros2016_towardseffective,
title = {Towards effective localization in dynamic environments},
author = {Dali Sun and Florian Geißer and Bernhard Nebel},
booktitle = {IROS 2016},
year = {2016}
}