  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.
