用波束成形提升毫米波雷达定位精度，解决传统方法分辨率低的问题。
Digital Beamforming Enhanced Radar Odometry
- 引入波束成形技术替代传统FFT处理，提升空间分辨率。
- 在多个公开数据集上验证，定位精度显著优于传统方法。
- 适合需要高精度雷达导航的自动驾驶系统研发人员。
雷达已成为自主导航的关键传感器，尤其在摄像头和激光雷达失效的复杂环境中。4D单芯片毫米波雷达系统因其低成本、低功耗并能提供空间与多普勒信息而备受关注。然而，大多数基于传统信号处理（如快速傅里叶变换）的单芯片雷达系统在检测中存在空间分辨率受限的问题，严重制约了基于雷达的里程计与同时定位与地图构建（SLAM）系统的性能。本文提出一种新型雷达信号处理流程，融合空域波束成形技术，并拓展至三维到达方向估计。通过在公开数据集上的实验，评估并对比了该方法与传统方法在不同场景下的结构精度及里程计准确性。结果表明，仅以该新流程替换标准FFT处理，即可实现更精确的雷达里程计。代码已开源于GitHub*。
原文摘要 · Abstract (English)
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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