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.
Enhancing vehicle localization by matching HD map with road marking detection
01.11.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
ROAD MARKING DETECTION METHOD AND ROAD MARKING DETECTION DEVICE
Europäisches Patentamt | 2024
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