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Vehicle Localization using 76GHz Omnidirectional Millimeter-Wave Radar for Winter Automated Driving*

机译:使用76GHz全向毫米波雷达进行冬季自动驾驶的车辆定位 *

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This paper presents the 76GHz MWR (Millimeter-Wave Radar)-based self-localization method for automated driving during snowfall. Previously, there were many LIDAR (Light Detection and Ranging)-based localization techniques proposed for high measurement accuracy and robustness to changes of day and night. However, they did not provide effective approaches for snow conditions because of sensing noise (i.e., environmental resistance) created by snowfall. Therefore, this paper developed a MWR-based map generation and a real-time localization method by modeling the uncertainties of error propagation. Quantitative evaluations are performed on driving data with and without snow conditions, using a LIDAR-based method as the baseline. Experimental results show that a lateral root mean square error of about 0. 25m can be obtained, regardless of the presence or absence of snowfall. Thus, it can be investigated that a potential performance of radar-based localization.
机译:本文提出了基于76GHz MWR(毫米波雷达)的自定位方法,用于降雪期间的自动驾驶。以前,已经提出了许多基于LIDAR(光检测和测距)的定位技术,以实现高测量精度和对日夜变化的鲁棒性。然而,由于感测到降雪产生的噪声(即,对环境的抵抗力),它们没有提供有效的降雪方法。因此,本文通过对误差传播的不确定性进行建模,开发了一种基于MWR的地图生成方法和实时定位方法。使用基于LIDAR的方法作为基准,对有雪和无雪情况下的行驶数据进行定量评估。实验结果表明,无论是否存在降雪,均方根均方根误差约为0. 25m。因此,可以调查基于雷达的定位的潜在性能。

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