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.
@article{arxiv.2308.10597,
title = {Doppler-aware Odometry from FMCW Scanning Radar},
author = {Fraser Rennie and David Williams and Paul Newman and Daniele De Martini},
journal= {arXiv preprint arXiv:2308.10597},
year = {2023}
}