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

Chandima Dedduwa Pathiranage; Keigo Watanabe; Kiyotaka Izumi

doi:10.1007/s00500-009-0409-1

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 journal › Article › peer-review

2 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	345-364
Number of pages	20
Journal	Soft Computing
Volume	14
Issue number	4
DOIs	https://doi.org/10.1007/s00500-009-0409-1
Publication status	Published - Jan 1 2010
Externally published	Yes

Keywords

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

ASJC Scopus subject areas

Software
Theoretical Computer Science
Geometry and Topology

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

abstract = "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.",

keywords = "Data association, Fuzzy Kalman filtering, Pseudolinear model, Simultaneous localization and mapping, T-S fuzzy model",

author = "Pathiranage, {Chandima Dedduwa} and Keigo Watanabe and Kiyotaka Izumi",

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AU - Pathiranage, Chandima Dedduwa

AU - Watanabe, Keigo

AU - Izumi, Kiyotaka

PY - 2010/1/1

Y1 - 2010/1/1

N2 - 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.

AB - 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.

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KW - Pseudolinear model

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KW - T-S fuzzy model

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