Dynamic vehicle tracking is an important module for Autonomous Land Vehicle (ALV) navigation in outdoor environments. The key step for a successful tracker is to accurately estimate the pose of the vehicle. In this paper, we present a novel real-time vehicle pose estimation algorithm based on the likelihood field model built on the Velodyne LIDAR data. The likelihood field model is adopted to weight the particles, which represent the potential poses, drawn around the location of the target vehicle. Importance sampling which is speeded up with the Scaling Series algorithm, is then exploited to choose the best particle as the final vehicle's pose. The performance of the algorithm is validated on the data collected by our own ALV in various urban environments.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Likelihood-Field-Model-Based Vehicle Pose Estimation with Velodyne


    Contributors:
    Chen, Tongtong (author) / Dai, Bin (author) / Liu, Daxue (author) / Fu, Hao (author) / Song, Jinze (author) / Wei, Chongyang (author)


    Publication date :

    2015-09-01


    Size :

    3869325 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Velodyne SLAM

    Moosmann, F. / Stiller, C. | IEEE | 2011


    Velodyne SLAM

    Moosmann, F. / Stiller, C. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2011


    Multiple Vehicle-like Target Tracking Based on the Velodyne LiDAR

    Zhang, Liang / Li, Qingquan / Li, Ming et al. | Tema Archive | 2013


    UAS TOPOGRAPHIC MAPPING WITH VELODYNE LiDAR SENSOR

    G. Jozkow / C. Toth / D. Grejner-Brzezinska | DOAJ | 2016

    Free access