  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.
