Doppler-aware Odometry from FMCW Scanning Radar

Abstract-- 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. We test our method with a novel custom dataset which is released with this work at https://ori.ox.ac.uk/publications/datasets. Index Terms-- radar odometry, doppler, navigation, dataset As considered deployment scenarios become more challenging, the detection methods and the sensors collecting data about a vehicle's surroundings must Figure 1: Radar scan from the RDD dataset. Currently, the primary sensors used by autonomous two regions extracted show the "zig-zag" pattern caused by vehicles are cameras and LiDAR: while these traditional the alternating modulation patterns - in conjunction with the sensors may perform adequately under favourable conditions, ego-vehicle speed.