Doppler-aware Odometry from FMCW Scanning Radar

2023/08/21 by Fraser Rennie, Rennie, Fraser, David Williams +5 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Gait Recognition and Analysis #Indoor and Outdoor Localization Technologies #Robotics (cs.RO) #Target Tracking and Data Fusion in Sensor Networks

paper · pdf · doi:10.48550/arxiv.2308.10597

openalex publication_date 2023/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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. 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.

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• RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles

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