Accurate and reliable train positioning stands as a cornerstone in railway train control system applications. In this paper, a seamless train positioning method based on visual position identification (VPR) is proposed. Our method uses on-board vision sensors to continuously capture images of the railway environment. Then, these images are processed by using the NetVLAD model to extract visual features. Subsequently, Euclidean distance is employed to conduct similarity retrieval on these features against pre-constructed railway image maps, facilitating precise positioning along the train line. This method has several obvious advantages. First, independence from external infrastructure: Unlike traditional methods reliant on trackside infrastructure such as physical transponders, our approach achieves precise positioning akin to a transponder without external dependencies. Second, decoupled position processes: Each image retrieval process operates independently, devoid of strong coupling relationships between positioning at adjacent frames. Third, robust adaptability to diverse environmental conditions: Experiments show that train location can be achieved in both open environment and tunnel scenes, and the coverage rate of effective train location reaches 98.2%, which proves the potential of this method in train location.


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    Title :

    A Seamless Train Positioning Method Based on Visual Place Recognition


    Contributors:
    Liu, Yongqiang (author) / Jiang, Wei (author) / Hu, Xiao (author) / Wang, Jian (author) / Cai, Baigen (author)


    Publication date :

    2024-09-24


    Size :

    531974 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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