The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset
Open accessAbstract
In this paper we present The Oxford Radar RobotCar Dataset, a new dataset for researching scene understanding using Millimetre-Wave FMCW scanning radar data. The target application is autonomous vehicles where this modality is robust to environmental conditions such as fog, rain, snow, or lens flare, which typically challenge other sensor modalities such as vision and LIDAR.(/P)(P)The data were gathered in January 2019 over thirty-two traversals of a central Oxford route spanning a total of 280 km of urban driving. It encompasses a variety of weather, traffic, and lighting conditions. This 4.7 TB dataset consists of over 240,000 scans from a Navtech CTS350-X radar and 2.4 million scans from two Velodyne HDL-32E 3D LIDARs; along with six cameras, two 2D LIDARs, and a GPS/INS receiver. In addition we release ground truth optimised radar odometry to provide an additional impetus to research in this domain. The full dataset is available for download at: ori.ox.ac.uk/datasets/radar-robotear-dataset.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152
1 year, 1000 km: The Oxford RobotCar dataset
Maddern, W., Pascoe, G., Linegar, C., & Newman, P. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364916679498 · Cited by 1,632 · cited 11×
“We include in this release a full set of extrinsic calibration data needed to utilise the additional Navtech and Velodyne sensors while the intrinsics and extrinsics of the sensors from [1] remain unchanged.”
BackgroundMethodResultInfluentialSelf-citationFAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
Cummins, M., & Newman, P. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908090961 · Cited by 1,472
Self-citation(2018) · arXiv (Cornell University) · Cited by 679
Ford Campus vision and lidar data set
Pandey, G., McBride, J. R., & Eustice, R. M. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364911400640 · Cited by 360
The Málaga urban dataset: High-rate stereo and LiDAR in a realistic urban scenario
Blanco-Claraco, J.-L., Moreno-Dueñas, F.-Á., & González-Jiménez, J. (2014) · The International Journal of Robotics Research · doi:10.1177/0278364913507326 · Cited by 262
Precise Ego-Motion Estimation with Millimeter-Wave Radar Under Diverse and Challenging Conditions
Cen, S. H., & Newman, P. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460687 · Cited by 214
Self-citationRadar-only ego-motion estimation in difficult settings via graph matching
Cen, S. H., & Newman, P. M. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793990 · Cited by 123
Self-citation
Cited by
Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425
Kim, G., Choi, S., & Kim, A. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3116424 · Cited by 319
A Survey on Safety-Critical Driving Scenario Generation—A Methodological Perspective
Ding, W., Xu, C., Arief, M., Lin, H., Li, B., & Zhao, D. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3259322 · Cited by 264
Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals
Qian, K., Zhu, S., Zhang, X., & Li, L. E. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00051 · Cited by 206
Metadata
- Authors
- Barnes, D., Gadd, M., Murcutt, P., Newman, P., & Posner, I.
- Venue
- 2020 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 6433-6438
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3090139470
- Cited by
- 429
- References
- 19
- FWCI
- 55.32
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset. https://app.cochat.ai/papers/10.1109/icra40945.2020.9196884
- The paper
- Barnes, D., Gadd, M., Murcutt, P., Newman, P., & Posner, I. (2020). The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset. 2020 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra40945.2020.9196884
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
