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：本文利用毫米波（mm-W）调频连续波（FMCW）扫描雷达的多普勒信息，旨在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（Neural Network, NN），直接从单次雷达扫描中回归前向速度；将该估计值与相关扫描匹配估计值融合，表明该方法能够改善因挑战性环境几何结构（如狭窄隧道）导致的差估计鲁棒性。我们使用一个新颖的自定义数据集测试了该方法，该数据集已随本文公开发布于 https://ori.ox.ac.uk/publications/datasets。