In this paper, a new method for mobile robot positioning is proposed. The method is a combination of particle filter (PF) and extended Kalman filter (EKF). Under normal driving situation, EKF is sufficient to estimate the vehicle's pose and location. Subject to external disturbances, EKF does not converge from time to time. PF is then introduced and the switching criteria are governed by the estimation confidence. We cluster the particles of PF into groups at the end of each iteration. The number of clusters is used as one of the parameters to determine whether the PF has converged. In the paper, the formulations and algorithms are illuminated and experimental results are also given and analyzed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Adaptive pose and location estimation for indoor mobile robot


    Contributors:
    Cheng Chen, (author) / Han Wang, (author)


    Publication date :

    2003-01-01


    Size :

    417769 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Adaptive Pose and Location Estimation for Indoor Mobile Robot

    Chen, C. / Wang, H. / IEEE | British Library Conference Proceedings | 2003


    ROBOT POSE ESTIMATION

    RAMANATHAN NARAYANAN / MEYER TIMON / RASAM ADITYA SHIWAJI et al. | European Patent Office | 2023

    Free access

    Indoor mobile robot attitude estimation with MEMS gyroscope

    Cechowicz Radosław | DOAJ | 2017

    Free access

    Indoor autonomous mobile robot

    ZHU JIANYANG / ZHANG XUYANG / JIANG LIN et al. | European Patent Office | 2021

    Free access

    Indoor navigation mobile robot

    GUO WANJIN / ZHAO WUDUAN / BIAN WANLONG et al. | European Patent Office | 2020

    Free access