Joint spatial- and Doppler-based ego-motion estimation for automotive radars

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI:10.1109/IVS.2015.7225789

M. Barjenbruch, Dominik Kellner, J. Klappstein, J. Dickmann, K. Dietmayer

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引用次数: 29

Abstract

An ego-motion estimation method based on the spatial and Doppler information obtained by an automotive radar is proposed. The estimation of the motion state vector is performed in a density-based framework. Compared to standard vehicle odometry the approach is capable to estimate the full two dimensional motion state with three degrees of freedom. The measurement of a Doppler radar sensor is represented as a mixture of Gaussians. This mixture is matched with the mixture of a previous measurement by applying the appropriate egomotion transformation. The parameters of the transformation are found by the optimization of a suitable join metric. Due to the Doppler information the method is very robust against disturbances by moving objects and clutter. It provides excellent results for highly nonlinear movements. Real world results of the proposed method are presented. The measurements are obtained by a 77GHz radar sensor mounted on a test vehicle. A comparison using a high-precision inertial measurement unit with differential GPS support is made. The results show a high accuracy in velocity and yaw-rate estimation.

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基于关节空间和多普勒的汽车雷达自运动估计

提出了一种基于汽车雷达获取的空间信息和多普勒信息的自运动估计方法。运动状态向量的估计是在基于密度的框架中进行的。与标准车辆里程计相比，该方法能够估计具有三个自由度的完整二维运动状态。多普勒雷达传感器的测量用高斯信号的混合表示。通过应用适当的自我运动变换，该混合与先前测量的混合相匹配。通过优化合适的连接度量来确定转换的参数。由于多普勒信息，该方法对运动物体和杂波的干扰具有很强的鲁棒性。它为高度非线性运动提供了极好的结果。给出了该方法的实际结果。测量结果由安装在测试车辆上的77GHz雷达传感器获得。采用差分GPS支撑的高精度惯性测量单元进行了比较。结果表明，该方法具有较高的速度和偏航率估计精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 IEEE Intelligent Vehicles Symposium (IV)

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