For highly and fully automated driving an accurate vehicle localization is crucial. At any time the position has to be known within an accuracy of a few decimeters, which can not be guaranteed by single GNSS measurements. Urban canyons scenarios lead to situations where the satellite visibility is low and effects like multi-path occur. We suggest using LiDAR sensors to augment localization.We use a feature-based map matching approach to estimate the vehicle position and orientation. The approach firstly detects pole-like and vertical planar objects, curb stones and lane markings in the measurements of automotive LiDAR sensors, which are integrated as standard in modern vehicels. The detected features are matched to a reference HD map. They are combined in a filter approach, in addition to GPS/IMU and vehicle odometry measurements. We tested our approach in an urban scenario and on a highway. The resulting accuracy ranges from 0.24 m (2D) in challenging urban areas to 0.12 m in lateral direction on a highway. The lateral error on a highway is always sufficiently small to allow the driving lane to be correctly determined.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Map Matching for Vehicle Localization Based on Serial Lidar Sensors


    Contributors:


    Publication date :

    2019-10-01


    Size :

    3124787 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Integrated Inertial-LiDAR-Based Map Matching Localization for Varying Environments

    Xia, Xin / Bhatt, Neel P. / Khajepour, Amir et al. | IEEE | 2023


    Persistent Homology in LiDAR-Based Ego-Vehicle Localization

    Akai, Naoki / Hirayama, Takatsugu / Murase, Hiroshi | IEEE | 2021



    LiDAR-based Cooperative Relative Localization

    Dong, Jiqian / Chen, Qi / Qu, Deyuan et al. | IEEE | 2023


    Efficient and Accurate Vehicle Localization Based on LiDAR Place Recognition

    Qimin, Xu / Xin, Zhao / Longjie, Liao et al. | BASE | 2023

    Free access