A fuzzy logic based approach to the SLAM problem using pseudolinear models with multiframe data association

Chandima Dedduwa Pathiranage, Keigo Watanabe, Kiyotaka Izumi

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

Abstract

This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using pseudolinear process and measurement models. Takagi-Sugeno (T-S) fuzzy model based on observation for nonlinear system is adopted to represent the process and measurement models of the vehicle-landmarks system. Using the Kalman filter theory, each local T-S model is filtered to find the local estimates. The linear combination of these local estimates gives the global estimate for the complete system. The simulation results prove that the new approach results in more anticipated performances, though nonlinearity is directly involved in the Kalman filter equations, compared to the conventional approach.

Original languageEnglish
Title of host publicationProceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08
Pages767-770
Number of pages4
Publication statusPublished - Dec 1 2008
Externally publishedYes
Event13th International Symposium on Artificial Life and Robotics, AROB 13th'08 - Oita, Japan
Duration: Jan 31 2008Feb 2 2008

Publication series

NameProceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08

Other

Other13th International Symposium on Artificial Life and Robotics, AROB 13th'08
Country/TerritoryJapan
CityOita
Period1/31/082/2/08

ASJC Scopus subject areas

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

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