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

Keigo Watanabe; Chandima Dedduwa Pathiranage; Kiyoaka Izumi

doi:10.1109/ISIE.2009.5217924

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

Keigo Watanabe, Chandima Dedduwa Pathiranage, Kiyoaka Izumi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 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. Fuzzy Kalman filtering of the SLAM problem (FKF-SLAM) is used in this paper together with newly proposed data association algorithm. An extended TSDA (ETSDA) method is introduced 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 (JPDA) method.

Original language	English
Title of host publication	Proceedings - IEEE ISIE 2009, IEEE International Symposium on Industrial Electronics
Pages	244-249
Number of pages	6
DOIs	https://doi.org/10.1109/ISIE.2009.5217924
Publication status	Published - Dec 1 2009
Event	IEEE International Symposium on Industrial Electronics, IEEE ISIE 2009 - Seoul, Korea, Republic of Duration: Jul 5 2009 → Jul 8 2009

Publication series

Name	IEEE International Symposium on Industrial Electronics

Other

Other	IEEE International Symposium on Industrial Electronics, IEEE ISIE 2009
Country/Territory	Korea, Republic of
City	Seoul
Period	7/5/09 → 7/8/09

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering

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10.1109/ISIE.2009.5217924

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Watanabe, K., Pathiranage, C. D., & Izumi, K. (2009). T-S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots. In Proceedings - IEEE ISIE 2009, IEEE International Symposium on Industrial Electronics (pp. 244-249). [5217924] (IEEE International Symposium on Industrial Electronics). https://doi.org/10.1109/ISIE.2009.5217924

T-S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots. / Watanabe, Keigo; Pathiranage, Chandima Dedduwa; Izumi, Kiyoaka.

Proceedings - IEEE ISIE 2009, IEEE International Symposium on Industrial Electronics. 2009. p. 244-249 5217924 (IEEE International Symposium on Industrial Electronics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Watanabe, K, Pathiranage, CD & Izumi, K 2009, T-S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots. in Proceedings - IEEE ISIE 2009, IEEE International Symposium on Industrial Electronics., 5217924, IEEE International Symposium on Industrial Electronics, pp. 244-249, IEEE International Symposium on Industrial Electronics, IEEE ISIE 2009, Seoul, Korea, Republic of, 7/5/09. https://doi.org/10.1109/ISIE.2009.5217924

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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. Fuzzy Kalman filtering of the SLAM problem (FKF-SLAM) is used in this paper together with newly proposed data association algorithm. An extended TSDA (ETSDA) method is introduced 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 (JPDA) method.",

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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. Fuzzy Kalman filtering of the SLAM problem (FKF-SLAM) is used in this paper together with newly proposed data association algorithm. An extended TSDA (ETSDA) method is introduced 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 (JPDA) method.

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. Fuzzy Kalman filtering of the SLAM problem (FKF-SLAM) is used in this paper together with newly proposed data association algorithm. An extended TSDA (ETSDA) method is introduced 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 (JPDA) method.

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

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