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Increasing FastSLAM accuracy for radar data by integrating the Doppler information

机译:通过集成多普勒信息提高雷达数据的FastSLAM精度

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This paper presents a modified FastSLAM approach for the specific application of radar sensors using the Doppler information to increase the localization and map accuracy. The developed approach is based on the FastSLAM 2.0 algorithm. It is shown how the FastSLAM 2.0 approach can be significantly improved by taking the Doppler information into account. Therefore, the modelled, so-called expected Doppler, and the measured Doppler are compared for every detection. Both, simulations and experiments on real world data show the increase in accuracy of the modified FastSLAM approach by incorporating the Doppler measurements of automotive radar sensors. The proposed algorithm is compared to the state-of-the-art FastSLAM 2.0 algorithm and the vehicle odometry, whereas profiles of an Automotive Dynamic Motion Analyzer serve as the reference.
机译:本文针对雷达传感器的特定应用,提出了一种改进的FastSLAM方法,该方法使用多普勒信息来提高定位和地图精度。开发的方法基于FastSLAM 2.0算法。它显示了如何通过考虑多普勒信息来显着改善FastSLAM 2.0方法。因此,对于每次检测,都要对建模的所谓的预期多普勒和测得的多普勒进行比较。对现实世界数据的仿真和实验均表明,通过结合汽车雷达传感器的多普勒测量,改进的FastSLAM方法的准确性有所提高。将该算法与最新的FastSLAM 2.0算法和车辆里程表进行了比较,而汽车动态运动分析仪的配置文件作为参考。

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