DC-Loc: Accurate Automotive Radar Based Metric Localization with Explicit Doppler Compensation

TL;DR

DC-Loc is a radar-based localization method that explicitly compensates for Doppler effects, significantly improving accuracy in automotive scenarios by restoring Doppler distortions and modeling measurement uncertainty.

Contribution

The paper introduces a novel Doppler compensation algorithm and measurement uncertainty modeling for radar-based localization, enhancing accuracy over existing methods.

Findings

Outperforms state-of-the-art by 25.2% in translation accuracy.

Achieves 5.6% improvement in rotation accuracy.

Validated on nuScenes dataset and CARLA simulator.

Abstract

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.