  Automotive mmWave radar has been widely used in the automotive industry due
to its small size, low cost, and complementary advantages to optical sensors
(e.g., cameras, LiDAR, etc.) in adverse weathers, e.g., fog, raining, and
snowing. On the other side, its large wavelength also poses fundamental
challenges to perceive the environment. Recent advances have made breakthroughs
on its inherent drawbacks, i.e., the multipath reflection and the sparsity of
mmWave radar's point clouds. However, the frequency-modulated continuous wave
modulation of radar signals makes it more sensitive to vehicles' mobility than
optical sensors. This work focuses on the problem of frequency shift, i.e., the
Doppler effect distorts the radar ranging measurements and its knock-on effect
on metric localization. We propose a new radar-based metric localization
framework, termed DC-Loc, which can obtain more accurate location estimation by
restoring the Doppler distortion. Specifically, we first design a new algorithm
that explicitly compensates the Doppler distortion of radar scans and then
model the measurement uncertainty of the Doppler-compensated point cloud to
further optimize the metric localization. Extensive experiments using the
public nuScenes dataset and CARLA simulator demonstrate that our method
outperforms the state-of-the-art approach by 25.2% and 5.6% improvements in
terms of translation and rotation errors, respectively.
