<i>OORD</i>: 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 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

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Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2024-08-26
DOI
https://doi.org/10.1109/tits.2024.3424984
Citations
13
Primary Topic
GNSS positioning and interference
Type
article
Field-Weighted Citation Impact
16.89

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article

OORD: The Oxford Offroad Radar Dataset

Valentina Muşat, Luke Robinson, Georgi Pramatarov, Letizia Marchegiani et al.
13 citations
IEEE Transactions on Intelligent Transportation Systems
GNSS positioning and interference
16.89
article

OORD: The Oxford Offroad Radar Dataset

Valentina Muşat, Luke Robinson, Georgi Pramatarov, Letizia Marchegiani, Matthew Gadd, Lars Kunze, Paul Newman, Marc Alexander Kühn, Daniele De Martini, Paul Murcutt, Matt Towlson, Matthew Widojo, Efimia Panagiotaki, Oliver Bartlett
article en
13 citations

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

IEEE Transactions on Intelligent Transportation SystemsVol. 25(11)
Science Oxford (GB)
Engineering and Physical Sciences Research Council
Openalex Percentile: Top 1%
GNSS positioning and interference
16.89
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