IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/905826.html
   My bibliography  Save this article

A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM

Author

Listed:
  • Jianjun Ni
  • Chu Wang
  • Xinnan Fan
  • Simon X. Yang

Abstract

Robot simultaneous localization and mapping (SLAM) problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF) based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach.

Suggested Citation

  • Jianjun Ni & Chu Wang & Xinnan Fan & Simon X. Yang, 2014. "A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-11, August.
    • Handle: RePEc:hin:jnlmpe:905826
    DOI: 10.1155/2014/905826
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2014/905826.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2014/905826.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2014/905826?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:905826. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.