OORD: The Oxford Offroad Radar Dataset

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Abstract

There is a growing academic interest as well as commercial exploitation of millimetre-wave scanning radar for autonomous vehicle localisation and scene understanding. Although several datasets to support this research area have been released, they are primarily focused on urban or semi-urban environments. Nevertheless, rugged offroad deployments are important application areas which also present unique challenges and opportunities for this sensor technology. Therefore, the Oxford Offroad Radar Dataset (OORD) presents data collected in the rugged Scottish highlands in extreme weather. The radar data we offer to the community are accompanied by GPS/INS reference – to further stimulate research in radar place recognition. In total we release over 90 GiB of radar scans as well as GPS and IMU readings by driving a diverse set of four routes over 11 forays, totalling approximately 154 km of rugged driving. This is an area increasingly explored in literature, and we therefore present and release examples of recent open-sourced radar place recognition systems and their performance on our dataset. This includes a learned neural network, the weights of which we also release. The data and tools are made freely available to the community at oxford-robotics-institute.github.io/oord-dataset.

Paper: https://ieeexplore.ieee.org/document/10648882

Dataset: oxford-robotics-institute.github.io/oord-dataset

Bibtex

@ARTICLE{OORD,
  author={Gadd, Matthew and De Martini, Daniele and Bartlett, Oliver and Murcutt, Paul and Towlson, Matt and Widojo, Matthew and Muşat, Valentina and Robinson, Luke and Panagiotaki, Efimia and Pramatarov, Georgi and Alexander Kühn, Marc and Marchegiani, Letizia and Newman, Paul and Kunze, Lars},
  journal={IEEE Transactions on Intelligent Transportation Systems}, 
  title={OORD: The Oxford Offroad Radar Dataset}, 
  year={2024},
  volume={25},
  number={11},
  pages={18779-18790},
  keywords={Radar;Meteorological radar;Robot sensing systems;Meteorology;Snow;Radar tracking;Autonomous vehicles;Radar;place recognition;localisation;odometry;positioning;robotics;autonomous vehicles},
  doi={10.1109/TITS.2024.3424984}}