Improved SLAM algorithm using fuzzy filter and curvature data association

Yan Jhang Shih; Chen-Chien James Hsu; Wei-Yen Wang; Yin Tien Wang

doi:10.1109/CACS.2014.7097172

Improved SLAM algorithm using fuzzy filter and curvature data association

Yan Jhang Shih, Chen-Chien James Hsu, Wei-Yen Wang, Yin Tien Wang

Department of Electrical Engineering

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

2 Citations (Scopus)

Abstract

The issue of simultaneous localization and mapping (SLAM) is an excellent technology. Normally, the current measurements need to be compared with all existing landmarks. However, the accuracy of the estimated location of the robot will decrease because of incorrect data association. To solve these problems, this paper presents a novel architecture for SLAM. The fuzzy filter and curvature data are used to filter current measurement to retain special measurements and avoid wrong landmarks. In addition, triangulation is used to improve the accuracy of the robot's location. The effectiveness of the proposed algorithm is showed by means of simulation results.

Original language	English
Title of host publication	CACS 2014 - 2014 International Automatic Control Conference, Conference Digest
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	113-116
Number of pages	4
ISBN (Electronic)	9781479945849
DOIs	https://doi.org/10.1109/CACS.2014.7097172
Publication status	Published - 2014 Apr 28
Event	2014 International Automatic Control Conference, CACS 2014 - Kaohsiung, Taiwan Duration: 2014 Nov 26 → 2014 Nov 28

Other

Other	2014 International Automatic Control Conference, CACS 2014
Country	Taiwan
City	Kaohsiung
Period	2014/11/26 → 2014/11/28

Keywords

FastSLAM
K-value curvatur
extended Kalman filter
fuzzy filter
particle filter

ASJC Scopus subject areas

Control and Systems Engineering

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10.1109/CACS.2014.7097172

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Improved SLAM algorithm using fuzzy filter and curvature data association. / Shih, Yan Jhang; Hsu, Chen-Chien James ; Wang, Wei-Yen; Wang, Yin Tien.

CACS 2014 - 2014 International Automatic Control Conference, Conference Digest. Institute of Electrical and Electronics Engineers Inc., 2014. p. 113-116 7097172.

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

Shih, YJ, Hsu, C-CJ , Wang, W-Y & Wang, YT 2014, Improved SLAM algorithm using fuzzy filter and curvature data association. in CACS 2014 - 2014 International Automatic Control Conference, Conference Digest., 7097172, Institute of Electrical and Electronics Engineers Inc., pp. 113-116, 2014 International Automatic Control Conference, CACS 2014, Kaohsiung, Taiwan, 2014/11/26. https://doi.org/10.1109/CACS.2014.7097172

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abstract = "The issue of simultaneous localization and mapping (SLAM) is an excellent technology. Normally, the current measurements need to be compared with all existing landmarks. However, the accuracy of the estimated location of the robot will decrease because of incorrect data association. To solve these problems, this paper presents a novel architecture for SLAM. The fuzzy filter and curvature data are used to filter current measurement to retain special measurements and avoid wrong landmarks. In addition, triangulation is used to improve the accuracy of the robot's location. The effectiveness of the proposed algorithm is showed by means of simulation results.",

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author = "Shih, {Yan Jhang} and Hsu, {Chen-Chien James} and Wei-Yen Wang and Wang, {Yin Tien}",

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doi = "10.1109/CACS.2014.7097172",

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booktitle = "CACS 2014 - 2014 International Automatic Control Conference, Conference Digest",

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N2 - The issue of simultaneous localization and mapping (SLAM) is an excellent technology. Normally, the current measurements need to be compared with all existing landmarks. However, the accuracy of the estimated location of the robot will decrease because of incorrect data association. To solve these problems, this paper presents a novel architecture for SLAM. The fuzzy filter and curvature data are used to filter current measurement to retain special measurements and avoid wrong landmarks. In addition, triangulation is used to improve the accuracy of the robot's location. The effectiveness of the proposed algorithm is showed by means of simulation results.

AB - The issue of simultaneous localization and mapping (SLAM) is an excellent technology. Normally, the current measurements need to be compared with all existing landmarks. However, the accuracy of the estimated location of the robot will decrease because of incorrect data association. To solve these problems, this paper presents a novel architecture for SLAM. The fuzzy filter and curvature data are used to filter current measurement to retain special measurements and avoid wrong landmarks. In addition, triangulation is used to improve the accuracy of the robot's location. The effectiveness of the proposed algorithm is showed by means of simulation results.

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KW - particle filter

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ER -

Improved SLAM algorithm using fuzzy filter and curvature data association

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Keywords

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