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.

翻译：本文探索利用毫米波（mm-W）调频连续波（FMCW）扫描雷达的多普勒信息，以提升里程计估计的鲁棒性和准确性。首先，将多普勒信息引入扫描掩膜处理过程，以增强相关扫描匹配。其次，我们训练一个神经网络（NN），通过单次雷达扫描直接回归前向速度；将该估计值与相关扫描匹配估计值融合，结果表明，在由狭窄隧道等挑战性环境几何结构导致的恶劣估计条件下，该方法具有更强的鲁棒性。我们通过一个新构建的自定义数据集测试了该方法，该数据集随本文发布于https://ori.ox.ac.uk/publications/datasets。