# Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?

arXiv: https://arxiv.org/abs/2404.01537 · HTML: https://mlsanity.com/paper/2404.01537/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2024.

## Built on by (most cited first, 4 of 4)

- [RaSCL: Radar to Satellite Crossview Localization](https://mlsanity.com/paper/2504.15899/) (2025)
- [RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles](https://mlsanity.com/paper/2507.00937/) (2025)
- [Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization](https://mlsanity.com/paper/2605.07041/) (2026)
- [Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking?](https://mlsanity.com/paper/2609.21000/) (2026)

## Abstract (arXiv)

Spinning, frequency-modulated continuous-wave (FMCW) radars with 360 degree coverage have been gaining popularity for autonomous-vehicle navigation. However, unlike `fixed' automotive radar, commercially available spinning radar systems typically do not produce radial velocities due to the lack of repeated measurements in the same direction and the fundamental hardware setup. To make these radial velocities observable, we modified the firmware of a commercial spinning radar to use triangular frequency modulation. In this paper, we develop a novel way to use this modulation to extract radial Doppler velocity measurements from consecutive azimuths of a radar intensity scan, without any data association. We show that these noisy, error-prone measurements contain enough information to provide good ego-velocity estimates, and incorporate these estimates into different modern odometry pipelines. We extensively evaluate the pipelines on over 110 km of driving data in progressively more geometrically challenging autonomous-driving environments. We show that Doppler velocity measurements improve odometry in well-defined geometric conditions and enable it to continue functioning even in severely geometrically degenerate environments, such as long tunnels.