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

Chandima Dedduwa Pathiranage, Lanka Udawatta, Keigo Watanabe, Kiyotaka Izumi

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

3 Citations (Scopus)

Abstract

This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using a pseudolinear measurement model of nonholonomic mobile robots. Takagi-Sugeno fuzzy model based on observation for nonlinear system is adopted to represent the process and measurement models of the vehicle-landmarks system. The complete system of the vehicle-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. 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 2007 3rd International Conference on Information and Automation for Sustainability, ICIAFS
Pages70-75
Number of pages6
DOIs
Publication statusPublished - Dec 1 2007
Externally publishedYes
Event2007 3rd International Conference on Information and Automation for Sustainability, ICIAFS - Melbourne, Australia
Duration: Dec 4 2007Dec 6 2007

Publication series

NameProceedings of the 2007 3rd International Conference on Information and Automation for Sustainability, ICIAFS

Other

Other2007 3rd International Conference on Information and Automation for Sustainability, ICIAFS
CountryAustralia
CityMelbourne
Period12/4/0712/6/07

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering

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