数字波束形成增强的雷达里程计
Digital Beamforming Enhanced Radar Odometry
AI总结：
本文提出融合空间域波束形成并支持三维 DOA 估计的雷达信号处理流程，以替代传统 FFT 处理，在公开数据集上提升了结构精度与多系统雷达里程计精度。
摘要：
雷达已成为自主导航中不可或缺的传感器，尤其是在相机和 LiDAR 传感器失效的挑战性环境中。4D 单芯片毫米波雷达系统尤其受到越来越多的关注，因为其能够以较低的硬件成本和功耗提供空间与多普勒信息。然而，大多数采用快速傅里叶变换等传统信号处理方法的单芯片雷达系统，在雷达探测中存在空间分辨率有限的问题，显著限制了基于雷达的里程计以及同步定位与地图构建（SLAM）系统的性能。本文提出一种新颖的雷达信号处理流程，融合空间域波束形成技术，并将其扩展至三维波达方向估计。我们使用公开数据集进行实验，评估并比较所提出的信号处理流程与传统方法的性能。这些测试专门聚焦于评估不同场景下的结构精度，以及测量不同雷达里程计系统中的里程计精度。本研究证明，只需用所提出的流程替代标准的基于 FFT 的处理，即可实现更精确的雷达里程计。代码发布于 GitHub*。
英文摘要：
Radar has become an essential sensor for autonomous navigation, especially in challenging environments where camera and LiDAR sensors fail. 4D single-chip millimeter-wave radar systems, in particular, have drawn increasing attention thanks to their ability to provide spatial and Doppler information with low hardware cost and power consumption. However, most single-chip radar systems using traditional signal processing, such as Fast Fourier Transform, suffer from limited spatial resolution in radar detection, significantly limiting the performance of radar-based odometry and Simultaneous Localization and Mapping (SLAM) systems. In this paper, we develop a novel radar signal processing pipeline that integrates spatial domain beamforming techniques, and extend it to 3D Direction of Arrival estimation. Experiments using public datasets are conducted to evaluate and compare the performance of our proposed signal processing pipeline against traditional methodologies. These tests specifically focus on assessing structural precision across diverse scenes and measuring odometry accuracy in different radar odometry systems. This research demonstrates the feasibility of achieving more accurate radar odometry by simply replacing the standard FFT-based processing with the proposed pipeline. The codes are available at GitHub*.
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