Presentado al IROS/2007 celebrado en San Diego (USA). ; This article presents a data fusion method which seeks to obtain better pose estimation of a mobile robot through obtaining a more accurate covariance uncertainty matrix. We seek to compute the state covariance without using the first-order linear approximations of the extended Kalman filter. We consider, unlike standard work done in error propagation and data fusion, the possible correlation between the different sensor pose estimates, odometry and DGPS for the present work, and the autocorrelation of some of the variables involved in the fusion (DGPS data, for the particular case herein presented). Computation of the covariances of each sensor data vector is presented so it takes into account the vehicle kinematics, and hence, its particular characteristics. In order to validate the presented approach, a real outdoor navigation experiment is presented fusing odometry and DGPS data. ; Peer Reviewed


    Access

    Download


    Export, share and cite



    Title :

    Outdoor robot navigation based on a probabilistic data fusion scheme


    Contributors:

    Publication date :

    2007-01-01



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Analysis of a sensor fusion hybrid solution for indoor/outdoor robot navigation

    Martí, Enrique David / García, Jesús / Molina, José M. | BASE | 2010

    Free access



    Robust dead reckoning system by fusion of odometry and gyro for mobile robot outdoor navigation

    Maeyama, S. / Ohya, A. / Yuta, S. | British Library Online Contents | 1997


    Robot Navigation Using a DR/GPS Data Fusion

    Zhang, Yuan Liang ;Park, Jong Ho ;Sel, Nam O | Trans Tech Publications | 2013