OORD: The Oxford Offroad Radar Dataset
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914810862501888 |
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02845 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OORD: The Oxford Offroad Radar Dataset 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 Robotics 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. |
| title | OORD: The Oxford Offroad Radar Dataset |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.02845 |