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Negative Information and Line Observations for Monte Carlo Localization (2008)
Todd Hester
and
Peter Stone
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.
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Citation:
In
IEEE International Conference on Robotics and Automation
, May 2008.
Bibtex:
@InProceedings{ICRA08-hester, title={Negative Information and Line Observations for Monte Carlo Localization}, author={Todd Hester and Peter Stone}, booktitle={IEEE International Conference on Robotics and Automation}, month={May}, url="http://www.cs.utexas.edu/users/ai-lab?ICRA08-hester", year={2008} }
People
Todd Hester
Postdoctoral Alumni
todd [at] cs utexas edu
Peter Stone
Faculty
pstone [at] cs utexas edu
Areas of Interest
Planning
Robotics
Labs
Learning Agents