This paper proposes a method that improves autonomous vehicles localization using a modification of probabilistic laser localization like Monte Carlo Localization (MCL) algorithm, enhancing the weights of the particles by adding Kalman filtered Global Navigation Satellite System (GNSS) information. GNSS data are used to improve localization accuracy in places with fewer map features and to prevent the kidnapped robot problems. Besides, laser information improves accuracy in places where the map has more features and GNSS higher covariance, allowing the approach to be used in specifically difficult scenarios for GNSS such as urban canyons. The algorithm is tested using KITTI odometry dataset proving that it improves localization compared with classic GNSS + Inertial Navigation System (INS) fusion and Adaptive Monte Carlo Localization (AMCL), it is also tested in the autonomous vehicle platform of the Intelligent Systems Lab (LSI), of the University Carlos III de of Madrid, providing qualitative results. ; Research supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-Rand RTI2018-096036-B-C21), Universidad Carlos III of Madrid through (PEAVAUTO-CM-UC3M) and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362).


    Access

    Download


    Export, share and cite



    Title :

    Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS



    Publication date :

    2020-06-02


    Remarks:

    AR/0000025768



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    A SURVEY ON 3D LIDAR LOCALIZATION FOR AUTONOMOUS VEHICLES

    Elhousni, Mahdi / Huang, Xinming | British Library Conference Proceedings | 2020


    A Survey on 3D LiDAR Localization for Autonomous Vehicles

    Elhousni, Mahdi / Huang, Xinming | IEEE | 2020


    A Survey on 3D LiDAR Localization for Autonomous Vehicles

    Elhousni, Mahdi / Huang, Xinming | ArXiv | 2020

    Free access

    LiDAR Based Classification Optimization of Localization Policies of Autonomous Vehicles

    Hamieh, Ismail / Myers, Ryan / Rahman, Taufiq | SAE Technical Papers | 2020


    LiDAR Based Classification Optimization of Localization Policies of Autonomous Vehicles

    Hamieh, Ismail / Myers, Ryan / Rahman, Taufiq | British Library Conference Proceedings | 2020