EFEAR-4D: Ego-Velocity Filtering for Efficient and Accurate 4D radar Odometry

TL;DR

EFEAR-4D is a novel, learning-free 4D radar odometry method that leverages Doppler velocity for accurate ego-velocity estimation, robust against noise and sparsity, outperforming existing approaches in diverse conditions.

Contribution

The paper introduces EFEAR-4D, a new efficient and accurate 4D radar odometry approach that utilizes Doppler velocity and dynamic object removal, with a new dataset highlighting radar height effects.

Findings

Achieves state-of-the-art localization accuracy in experiments.

Robust against noise and point cloud sparsity across environments.

Demonstrates the impact of radar height on point cloud quality.

Abstract

Odometry is a crucial component for successfully implementing autonomous navigation, relying on sensors such as cameras, LiDARs and IMUs. However, these sensors may encounter challenges in extreme weather conditions, such as snowfall and fog. The emergence of FMCW radar technology offers the potential for robust perception in adverse conditions. As the latest generation of FWCW radars, the 4D mmWave radar provides point cloud with range, azimuth, elevation, and Doppler velocity information, despite inherent sparsity and noises in the point cloud. In this paper, we propose EFEAR-4D, an accurate, highly efficient, and learning-free method for large-scale 4D radar odometry estimation. EFEAR-4D exploits Doppler velocity information delicately for robust ego-velocity estimation, resulting in a highly accurate prior guess. EFEAR-4D maintains robustness against point-cloud sparsity and noises across diverse environments through dynamic object removal and effective region-wise feature extraction. Extensive experiments on two publicly available 4D radar datasets demonstrate state-of-the-art reliability and localization accuracy of EFEAR-4D under various conditions. Furthermore, we have collected a dataset following the same route but varying installation heights of the 4D radar, emphasizing the significant impact of radar height on point cloud quality - a crucial consideration for real-world deployments. Our algorithm and dataset will be available soon at https://github.com/CLASS-Lab/EFEAR-4D.