Expectancy-based robot localization through context evaluation

Maria E. Messen*, Gert Kootstra, Sjoerd De Jong, Tjeerd C. Andringa

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

4 Citations (Scopus)

Abstract

Agents that operate in a real-world environment have to process an abundance of information, which may be ambiguous or noisy. We present a method inspired by cognitive research that keeps track of sensory information, and interprets it with knowledge of the context. We test this model on visual information from the real-world environment of a mobile robot in order to improve its self-localization. We use a topological map to represent the environment, which is an abstract representation of distinct places and the connections between them. Expectancies of the place of the robot on the map are combined with evidence from observations to reach the best prediction of the next place of the robot. These expectancies make a place prediction more robust to ambiguous and noisy observations. Results of the model operating on data gathered by a mobile robot confirm that context evaluation improves localization compared to a data-driven model.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Artificial Intelligence, ICAI 2009
PublisherIEEE Xplore
Pages371-377
Number of pages7
ISBN (Print)9781601321091
Publication statusPublished - 1 Dec 2009
Externally publishedYes
Event2009 International Conference on Artificial Intelligence, ICAI 2009 - Las Vegas, NV, United States
Duration: 13 Jul 200916 Jul 2009

Publication series

NameProceedings of the 2009 International Conference on Artificial Intelligence, ICAI 2009
Volume1

Conference

Conference2009 International Conference on Artificial Intelligence, ICAI 2009
CountryUnited States
CityLas Vegas, NV
Period13/07/0916/07/09

Keywords

  • Cognitive science
  • Knowledge network
  • Robot localization
  • Spreading activation

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