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Park marking-based vehicle self-localization with a fisheye topview system

机译:带有鱼眼顶视图系统的基于公园标记的车辆自定位

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Accurately self-localizing a vehicle is of high importance as it allows to robustify nearly all modern driver assistance functionality, e.g., lane keeping and coordinated autonomous driving maneuvers. We examine vehicle self-localization relying only on video sensors, in particular, a system of four fisheye cameras providing a view surrounding the car, a setup currently growing popular in upper-class cars. The presented work aims at an autonomous parking scenario. The method is based on park markings as orientation marks since they can be found in nearly every parking deck and require only little additional preparation. Our contribution is twofold: (1) we present a new real-time capable image processing pipeline for topview systems extracting park markings and show how to obtain a reliable and accurate ego pose and ego motion estimation given a coarse pose as starting point. (2) The aptitude of this often neglected sensor array for vehicle self-localization is demonstrated. Experimental evaluation yields a precision of 0.15 +/- 0.18 m and 2.01 degrees +/- 1.91 degrees.
机译:车辆的精确自定位非常重要,因为它可以使几乎所有现代驾驶员辅助功能(例如,车道保持和协调的自动驾驶操作)稳定化。我们仅依靠视频传感器来检查车辆的自我定位,特别是一个由四个鱼眼摄像头组成的系统,该摄像头可提供围绕汽车的视野,这种设置目前在高级汽车中越来越流行。提出的工作针对自动泊车场景。该方法基于公园标记作为方向标记,因为它们几乎可以在每个停车甲板中找到,并且只需要很少的额外准备即可。我们的贡献是双重的:(1)我们提出了一种用于顶视图系统的新的实时功能图像处理管道,该系统提取了公园标记,并展示了如何在以粗略姿势为起点的情况下获得可靠,准确的自我姿势和自我运动估计。 (2)证明了这种经常被忽略的传感器阵列在车辆自定位方面的能力。实验评估得出的精度为0.15 +/- 0.18 m和2.01 +/- 1.91度。

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