# Doppler-aware Odometry from FMCW Scanning Radar

arXiv: https://arxiv.org/abs/2308.10597 · HTML: https://mlsanity.com/paper/2308.10597/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2023.

## Built on by (most cited first, 2 of 2)

- [RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles](https://mlsanity.com/paper/2507.00937/) (2025)
- [Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking?](https://mlsanity.com/paper/2609.21000/) (2026)

## Abstract (arXiv)

This work explores Doppler information from a millimetre-Wave (mm-W) Frequency-Modulated Continuous-Wave (FMCW) scanning radar to make odometry estimation more robust and accurate. Firstly, doppler information is added to the scan masking process to enhance correlative scan matching. Secondly, we train a Neural Network (NN) for regressing forward velocity directly from a single radar scan; we fuse this estimate with the correlative scan matching estimate and show improved robustness to bad estimates caused by challenging environment geometries, e.g. narrow tunnels. We test our method with a novel custom dataset which is released with this work at https://ori.ox.ac.uk/publications/datasets.