Digital Beamforming Enhanced Radar Odometry

TL;DR

The paper tackles the limited DoA resolution of FFT-based processing in single-chip 4D mmWave radar for radar odometry/SLAM by introducing a digital beamforming pipeline that enables 3D DoA estimation and Doppler extraction. It adopts a two-stage Capon (MVDR) beamforming strategy to derive azimuth and elevation DoAs, followed by Doppler estimation, all within a spatial-domain processing framework. Experimental results on the ColoRadar dataset show that the proposed approach produces higher-quality 3D radar point clouds and yields notable improvements in translation accuracy for radar-inertial odometry, with rotation benefits more modest due to the baselines used. The work demonstrates practical gains for robust navigation in challenging environments and provides open-source code for replication.

Abstract

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*.

Figures (7)

• Figure 1: Comparison of radar signal processing pipelines.
• Figure 2: System overview of the proposed pipeline.
• Figure 3: MIMO radar geometric model.
• Figure 4: DoA estimation and Doppler estimation.
• Figure 5: Boxplot of the RPE in all sequences.