nao kickoff

Negative Information and Line Observations for Monte Carlo Localization

Todd Hester and Peter Stone. Negative Information and Line Observations for Monte Carlo Localization. In IEEE International Conference on Robotics and Automation, May 2008.
ICRA 2008

Download

[PDF]128.7kB  [postscript]501.7kB  

Abstract

Localization is a very important problem in robotics and is critical to many tasks performed on a mobile robot. In order to localize well in environments with few landmarks, a robot must make full use of all the information provided to it. This paper moves towards this goal by studying the effects of incorporating line observations and negative information into the localization algorithm. We extend the general Monte Carlo localization algorithm to utilize observations of lines such as carpet edges. We also make use of the information available when the robot expects to see a landmark but does not, by incorporating negative information into the algorithm. We compare our implementations of these ideas to previous similar approaches and demonstrate the effectiveness of these improvements through localization experiments performed both on a Sony AIBO ERS-7 robot and in simulation.

BibTeX Entry

@InProceedings{ICRA08-hester,
author="Todd Hester and Peter Stone",
title="Negative Information and Line Observations for Monte Carlo Localization",
booktitle="{IEEE} International Conference on Robotics and Automation",
location="Pasadena, CA",
month="May",
year="2008",
abstract="Localization is a very important problem in
robotics and is critical to many tasks performed on a mobile
robot. In order to localize well in environments with few
landmarks, a robot must make full use of all the information
provided to it. This paper moves towards this goal by studying
the effects of incorporating line observations and negative
information into the localization algorithm. We extend the general
Monte Carlo localization algorithm to utilize observations of
lines such as carpet edges. We also make use of the information
available when the robot expects to see a landmark but does
not, by incorporating negative information into the algorithm.
We compare our implementations of these ideas to previous
similar approaches and demonstrate the effectiveness of these
improvements through localization experiments performed both
on a Sony AIBO ERS-7 robot and in simulation.",
}

Valid CSS!
Valid XHTML 1.0!