arXiv 2107.096072021-07-20eess.SP

一种基于车载雷达的车辆位姿估计新方法

A Novel Approach to Vehicle Pose Estimation using Automotive Radar

Martijn Heller, Nikita Petrov, Alexander Yarovoy

摘要：

本文提出了一套新的扫描匹配技术的车辆姿态估计使用汽车雷达测量。所提出的方法修改了正态分布变换（NDT）-一种最先进的扫描匹配SLAM技术，广泛用于基于激光雷达的定位-以考虑雷达环境感知的特定方面。首先，极坐标无损检测（PNDT）是通过解决在极坐标系，自然雷达测量的无损检测问题。测量不确定度和它们在扫描匹配算法中的表示之间实现了更好的协议。其次，扩展PNDT考虑到多普勒测量-多普勒极无损检测（DPNDT）-提出。第三，在优化过程中加入检测目标的信噪比，以减小RCS起伏的影响。在数值模拟和实际数据处理中证明了对传统NDT的改进，显示出根据情况将定位误差降低3至5倍的能力，计算复杂性的增加可以忽略不计。最后，提出了一种具有补偿阵列波束形成中角偏差能力的DPNDT扩展。仿真结果和实际资料处理表明，该方法可以达到$0.1^\circ$的实时校正精度。

英文摘要：

This paper presents a set of novel scan-matching techniques for vehicle pose estimation using automotive radar measurements. The proposed approach modifies the Normal Distributions Transform (NDT) -- a state-of-the-art scan-matching SLAM technique, widely used in lidar-based localization -- to account for particular aspects of radar environment perception. First, the polar NDT (PNDT) is introduced by solving the NDT problem in the polar coordinate system, natural for radar measurements. A better agreement between the measurement uncertainties and their representation in the scan-matching algorithm is achieved. Second, the extension of PNDT to take into account the Doppler measurements -- Doppler polar NDT (DPNDT) -- is proposed. Third, the SNR of detected targets is added to the optimization procedure to minimize the impact of RCS fluctuation. The improvement over the conventional NDT is demonstrated in numerical simulations and real data processing, showing the ability to decrease the localization error by a factor of 3 to 5, depending on the scenario, with a negligible increase in computational complexity. Finally, a DPNDT extension with the capability to compensate angular bias in array beam-forming is presented. Simulation results and real data processing show the possibility to correct it with the accuracy of $0.1^\circ$ almost in real-time.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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