OORD: The Oxford Offroad Radar Dataset

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 environ...

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Published inarXiv.org
Main Authors Gadd, Matthew, De Martini, Daniele, Bartlett, Oliver, Murcutt, Paul, Towlson, Matt, Widojo, Matthew, Muşat, Valentina, Robinson, Luke, Panagiotaki, Efimia, Pramatarov, Georgi, Kühn, Marc Alexander, Marchegiani, Letizia, Newman, Paul, Kunze, Lars
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 05.03.2024
Subjects
Datasets
Millimeter waves
Neural networks
Radar data
Recognition
Robotics
Scene analysis
Urban environments
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Summary: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 90GiB of radar scans as well as GPS and IMU readings by driving a diverse set of four routes over 11 forays, totalling approximately 154km 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 https://oxford-robotics-institute.github.io/oord-dataset.
ISSN:2331-8422