DRO: Doppler-Aware Direct Radar Odometry

2025-04-29

AI Summary

To address pose estimation degradation in millimeter-wave radar SLAM under geometry-deprived environments (e.g., tunnels), caused by motion distortion and Doppler-induced measurement distortion, this paper proposes an SE(2) direct radar odometry method. It performs scan-to-local-map registration directly on raw intensity measurements from FMCW radar sweeps—bypassing feature extraction or point cloud generation. We introduce, for the first time, a frequency-modulation constraint that explicitly incorporates observable radial Doppler velocity, jointly modeling motion and Doppler distortions. The method further achieves tight coupling with gyroscope measurements via joint optimization. Evaluated on the Boreas dataset, it reduces relative translational error from 0.26% to 0.18%. Real-world validation spans 250 km of urban roads and a 1.5-hour off-road sequence, demonstrating real-time performance. The implementation is open-sourced.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

A renaissance in radar-based sensing for mobile robotic applications is underway. Compared to cameras or lidars, millimetre-wave radars have the ability to `see' through thin walls, vegetation, and adversarial weather conditions such as heavy rain, fog, snow, and dust. In this paper, we propose a novel SE(2) odometry approach for spinning frequency-modulated continuous-wave radars. Our method performs scan-to-local-map registration of the incoming radar data in a direct manner using all the radar intensity information without the need for feature or point cloud extraction. The method performs locally continuous trajectory estimation and accounts for both motion and Doppler distortion of the radar scans. If the radar possesses a specific frequency modulation pattern that makes radial Doppler velocities observable, an additional Doppler-based constraint is formulated to improve the velocity estimate and enable odometry in geometrically feature-deprived scenarios (e.g., featureless tunnels). Our method has been validated on over 250km of on-road data sourced from public datasets (Boreas and MulRan) and collected using our automotive platform. With the aid of a gyroscope, it outperforms state-of-the-art methods and achieves an average relative translation error of 0.26% on the Boreas leaderboard. When using data with the appropriate Doppler-enabling frequency modulation pattern, the translation error is reduced to 0.18% in similar environments. We also benchmarked our algorithm using 1.5 hours of data collected with a mobile robot in off-road environments with various levels of structure to demonstrate its versatility. Our real-time implementation is publicly available: https://github.com/utiasASRL/dro.

Problem

• Develops SE(2) odometry for spinning radar without feature extraction
• Estimates trajectory by correcting motion and Doppler scan distortions
• Enhances velocity accuracy in featureless environments using Doppler constraints

Innovation

• Direct scan-to-map registration using radar intensity
• Continuous trajectory estimation with Doppler correction
• Doppler-based constraint for featureless environments