T-S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots

Chandima Dedduwa Pathiranage, Keigo Watanabe, Kiyotaka Izumi

Research output: Contribution to journalArticle

2 Citations (Scopus)


This paper describes a Takagi-Sugeno (T-S) fuzzy model adopted solution to the simultaneous localization and mapping (SLAM) problem with two-sensor data association (TSDA) method. Nonlinear process model and observation model are formulated as pseudolinear models and rewritten with a composite model whose local models are linear according to T-S fuzzy model. Combination of these local state estimates results in global state estimate. This paper introduces an extended TSDA (ETSDA) method for the SLAM problem in mobile robot navigation based on an interior point linear programming (LP) approach. Simulation results are given to demonstrate that the ETSDA method has low computational complexity and it is more accurate than the existing single-scan joint probabilistic data association method. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem than the conventional extended Kalman filter - SLAM algorithm.

Original languageEnglish
Pages (from-to)345-364
Number of pages20
JournalSoft Computing
Issue number4
Publication statusPublished - 2010
Externally publishedYes



  • Data association
  • Fuzzy Kalman filtering
  • Pseudolinear model
  • Simultaneous localization and mapping
  • T-S fuzzy model

ASJC Scopus subject areas

  • Software
  • Geometry and Topology
  • Theoretical Computer Science

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