{"url":"/dataset/oxford-radar-robotcar-dataset","name":"Oxford Radar RobotCar Dataset","full_name":null,"description_markdown":"The Oxford Radar RobotCar Dataset is a radar extension to The Oxford RobotCar Dataset. It has been extended with data from a Navtech CTS350-X Millimetre-Wave FMCW radar and Dual Velodyne HDL-32E LIDARs with optimised ground truth radar odometry for 280 km of driving around Oxford, UK (in addition to all sensors in the original Oxford RobotCar Dataset).\r\n\r\nSource: [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](/paper/the-oxford-radar-robotcar-dataset-a-radar)","description_withheld":null,"homepage":"http://ori.ox.ac.uk/datasets/radar-robotcar-dataset","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/the-oxford-radar-robotcar-dataset-a-radar","title":"The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset","first_author":null,"url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"lidar absolute pose regression","url":"/task/lidar-absolute-pose-regression","datasets_with_task":"/datasets/task/lidar-absolute-pose-regression"},{"name":"Weather Forecasting","url":"/task/weather-forecasting","datasets_with_task":"/datasets/task/weather-forecasting"},{"name":"Visual Localization","url":"/task/visual-localization","datasets_with_task":"/datasets/task/visual-localization"},{"name":"Translation","url":"/task/translation","datasets_with_task":"/datasets/task/translation"},{"name":"Radar odometry","url":"/task/radar-odometry","datasets_with_task":"/datasets/task/radar-odometry"}],"languages":[],"variants":["Oxford Radar RobotCar Dataset","Oxford Radar RobotCar (Full-6)"],"data_loaders":[],"num_papers_in_archive":28,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/visual-localization-on-oxford-radar-robotcar","task":"Visual Localization","dataset_variant":"Oxford Radar RobotCar (Full-6)","rows":16,"metrics":["Mean Translation Error"],"first_row_in_archive_order":{"model":"LightLoc","paper":"/paper/lightloc-learning-outdoor-lidar-localization","metrics":{"Mean Translation Error":"2.67"},"code_links":[{"title":"liw95/lightloc","url":"https://github.com/liw95/lightloc"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/lidar-absolute-pose-regression-on-oxford-2","task":"lidar absolute pose regression","dataset_variant":"Oxford Radar RobotCar (Full-6)","rows":1,"metrics":["Mean Translation/Rotation Error (m/degree)"],"first_row_in_archive_order":{"model":"HypLiLoc","paper":"/paper/hypliloc-towards-effective-lidar-pose","metrics":{"Mean Translation/Rotation Error (m/degree)":"6.00 / 1.31"},"code_links":[{"title":"sijieaaa/hypliloc","url":"https://github.com/sijieaaa/hypliloc"},{"title":"sijieaaa/robustloc","url":"https://github.com/sijieaaa/robustloc"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/radar-odometry-on-oxford-radar-robotcar","task":"Radar odometry","dataset_variant":"Oxford Radar RobotCar Dataset","rows":1,"metrics":["translation error [%]"],"first_row_in_archive_order":{"model":"CFEAR-3-s4","paper":"/paper/cfear-radarodometry-conservative-filtering-1","metrics":{"translation error [%]":"1.31"},"code_links":[{"title":"dan11003/CFEAR_Radarodometry_code_public","url":"https://github.com/dan11003/CFEAR_Radarodometry_code_public"},{"title":"dan11003/CFEAR_Radarodometry","url":"https://github.com/dan11003/CFEAR_Radarodometry"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/translation-on-oxford-radar-robotcar-dataset","task":"Translation","dataset_variant":"Oxford Radar RobotCar Dataset","rows":1,"metrics":["translation error [%]"],"first_row_in_archive_order":{"model":"CFEAR-3-s50","paper":"/paper/cfear-radarodometry-conservative-filtering-1","metrics":{"translation error [%]":"1.09"},"code_links":[{"title":"dan11003/CFEAR_Radarodometry_code_public","url":"https://github.com/dan11003/CFEAR_Radarodometry_code_public"},{"title":"dan11003/CFEAR_Radarodometry","url":"https://github.com/dan11003/CFEAR_Radarodometry"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/lightloc-learning-outdoor-lidar-localization","title":"LightLoc: Learning Outdoor LiDAR Localization at Light Speed","date":"2025-03-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lisa-lidar-localization-with-semantic","title":"LiSA: LiDAR Localization with Semantic Awareness","date":"2024-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/diffloc-diffusion-model-for-outdoor-lidar","title":"DiffLoc: Diffusion Model for Outdoor LiDAR Localization","date":"2024-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hypliloc-towards-effective-lidar-pose","title":"HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion","date":"2023-04-03","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/sgloc-scene-geometry-encoding-for-outdoor","title":"SGLoc: Scene Geometry Encoding for Outdoor LiDAR Localization","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lidar-based-localization-using-universal","title":"LiDAR-based localization using universal encoding and memory-aware regression","date":"2022-08-01","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/cfear-radarodometry-conservative-filtering-1","title":"CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry","date":"2021-09-16","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/learning-multi-scene-absolute-pose-regression","title":"Learning Multi-Scene Absolute Pose Regression with Transformers","date":"2021-03-21","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":10,"samples_unverified":10,"pointer_only_for_licence":20,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mvloc-multimodal-variational-geometry-aware","title":"VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization","date":"2020-03-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/prior-guided-dropout-for-robust-visual","title":"Prior Guided Dropout for Robust Visual Localization in Dynamic Environments","date":"2019-10-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/atloc-attention-guided-camera-localization","title":"AtLoc: Attention Guided Camera Localization","date":"2019-09-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/190503304","title":"Deep Closest Point: Learning Representations for Point Cloud Registration","date":"2019-05-08","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pointnetvlad-deep-point-cloud-based-retrieval","title":"PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition","date":"2018-04-10","rows_on_this_dataset":1,"code_links":6,"syntology":null},{"paper":"/paper/geometry-aware-learning-of-maps-for-camera","title":"Geometry-Aware Learning of Maps for Camera Localization","date":"2017-12-09","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":23,"samples_ran":13,"samples_unverified":10,"pointer_only_for_licence":23,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}