Accurate and robust vehicle localization, a fundamental task for autonomous driving, is still a challenging problem especially in GPS-denied scenarios. Recently, high definition (HD) map suggests a promising solution. However, the matching between online sensed data and HD map is difficult and time-consuming. In our work, road markings are selected as landmarks due to salient appearance features. Based on the detection results of road markings from the vehicle-borne images, the points of edge lines are employed to fit straight lines with RANSAC for outlier removal. The distances between the ego-vehicle and the fitted edge lines can be computed with camera-vehicle calibration in advance. Subsequently, the point-to-line distances from the sensed data are mapped into global linear constraints on the vehicle’s positions with the support of a lane-level HD map, which provides centimeter-level coordinates of road markings. Finally, the distances and the localization from the integrated navigation system (INS) are fused with the proposed linear Kalman filter based on the second-order Markov model (KF-MM2). The proposed method has been verified in different daily driving scenarios. Experimental results demonstrate that our method can achieve good performance with an average localization error of 0.53 m and a standard deviation of 0.17 m.


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    Titel :

    Enhancing vehicle localization by matching HD map with road marking detection


    Beteiligte:
    Zhou, Zhe (Autor:in) / Hu, Zhaozheng (Autor:in) / Li, Na (Autor:in) / Lai, Guoliang (Autor:in)


    Erscheinungsdatum :

    01.11.2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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