Feature based estimation for mapping robot environments using fuzzy Kalman filter

Chandima Dedduwa Pathiranage, Keigo Watanabe, Kiyotaka Izumi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper introduces a fuzzy Kalman filter based approach for mapping robot environments. Takagi-Sugeno fuzzy models for nonlinear system are adopted to represent the vehicle and landmarks state equations. The complete system of the vehicle and landmarks model is decomposed into several linear models. Using the Kalman filter theory, each local model is filtered to find the local estimates. The linear combination of these local estimates gives the global estimate for the complete system. This estimator is simulated using Matlab for the vehicle-landmark system and results prove that the new approach can accurately map the environment.

Original languageEnglish
Title of host publicationProceedings of the 12th International Symposium on Artificial Life and Robotics, AROB 12th'07
Pages748-751
Number of pages4
Publication statusPublished - Dec 1 2007
Externally publishedYes
Event12th International Symposium on Artificial Life and Robotics, AROB 12th'07 - Oita, Japan
Duration: Jan 25 2007Jan 27 2007

Publication series

NameProceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07

Other

Other12th International Symposium on Artificial Life and Robotics, AROB 12th'07
CountryJapan
CityOita
Period1/25/071/27/07

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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  • Cite this

    Pathiranage, C. D., Watanabe, K., & Izumi, K. (2007). Feature based estimation for mapping robot environments using fuzzy Kalman filter. In Proceedings of the 12th International Symposium on Artificial Life and Robotics, AROB 12th'07 (pp. 748-751). (Proceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07).