Improved SLAM algorithm using fuzzy filter and curvature data association

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

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

2 Citations (Scopus)


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 languageEnglish
Title of host publicationCACS 2014 - 2014 International Automatic Control Conference, Conference Digest
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781479945849
Publication statusPublished - 2014 Apr 28
Event2014 International Automatic Control Conference, CACS 2014 - Kaohsiung, Taiwan
Duration: 2014 Nov 262014 Nov 28


Other2014 International Automatic Control Conference, CACS 2014


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

ASJC Scopus subject areas

  • Control and Systems Engineering


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