In urban environment, we need accurate and precise estimation of vehicle state for real time navigation and control. This paper presents an architecture to fuse different data from onboard sensors to estimate the vehicle state when observations are noisy. We are trying to compensate the GPS errors by data fusion from different sensors in a probabilistic way. A particle filter with joint observation model has been proposed to real timely estimate the vehicle state. An adaptive joint observation model has been developed to fuse different observations according to accuracy and reliability of the corresponding sensor. Finally a navigation architecture has been proposed for fully autonomous driving with dynamic obstacles. Experiments with real vehicle show the proposed method is able to estimate the vehicle state precisely when the individual observations fail to be enough accurate.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-sensor data fusion for autonomous vehicle navigation through adaptive particle filter


    Contributors:


    Publication date :

    2010-06-01


    Size :

    1141851 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multi-sensor data fusion for autonomous vehicle navigation through adaptive particle filter

    Hossein, Tehrani Nik Nejad / Mita, Seiichi / Han, Long | Tema Archive | 2010


    Multi-Sensor Data Fusion for Autonomous Vehicle Navigation through Adaptive Particle Filter, pp. 752-759

    Tehrani Nik Najad, H. / Mita, S. / Han, L. et al. | British Library Conference Proceedings | 2010



    Sensor and Navigation Data Fusion for an Autonomous Vehicle

    Becker, J. C. / Simon, A. / IEEE | British Library Conference Proceedings | 2000


    Particle filter for sensor fusion in a land vehicle navigation system

    Yang, Ning / Tian, Wei Feng / Jin, Zhi Hua et al. | Tema Archive | 2005