A vehicle-borne LiDAR based metro rail detection methodology in metro system is proposed. To begin with, the 2D occupancy grids map in XOY plane is generated from the 3D point cloud and those rail-related points are collected to set up the rail points set. Subsequently, false alarms in the rail points set are excluded in XOZ plane. RANSAC-based line fitting is performed and those outliers of the fitted line are considered as false alarms and thus excluded. Finally, the parameters of rail tracks are estimated with a hybrid method composed of PPHT, RANSAC and CLEAN, fulfilling the task of rail detection. An extra hypothesis points set for RANSAC is created by PPHT and parallelism lines verification to avoid shortening of latter pairs of rail tracks and achieve further removal of false alarms. During the RANSAC based estimation process, the constraint of gauge between pair-wise tracks are concerned and kept, and the chord-length distributions are adopted for overfitting elimination. The CLEAN operation is repeated to seek for multiple pairs of tracks. The proposed methodology has achieved precision of 99.14% and recall of 96.47% on data acquired in field experiments, convincing the effectiveness in the field of rail detection in metro system.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Metro Rail Detection Based on Vehicle-Borne LiDAR


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liang, Jianying (editor) / Jia, Limin (editor) / Qin, Yong (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Zeng, Xiang (author) / Leng, Binghan (author) / Jiang, Guotao (author) / Pi, Zhichao (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Publication date :

    2022-02-19


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Metro Rail Detection Based on Vehicle-Borne LiDAR

    Zeng, Xiang / Leng, Binghan / Jiang, Guotao et al. | British Library Conference Proceedings | 2022


    Metro Rail Systems

    Guerrieri, Marco | Springer Verlag | 2023



    Rail-mounted area manpower transport vehicle for metro vehicle maintenance

    ZHAO XINYU / CHANG MIAO / LI ZENGFENG et al. | European Patent Office | 2024

    Free access

    Structure-borne Noise Differences of Metro Vehicle Running on Different Tracks

    Zhang, Yunfei / Li, Li / Zhu, Qian | Springer Verlag | 2023