# The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset

arXiv: https://arxiv.org/abs/1909.01300 · HTML: https://mlsanity.com/paper/1909.01300/

## Key facts

- Cited by 134 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 30 of 134)

- [TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving](https://mlsanity.com/paper/2204.13483/) (2022)
- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [3D Object Detection from Images for Autonomous Driving: A Survey](https://mlsanity.com/paper/2202.02980/) (2022)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications](https://mlsanity.com/paper/2104.02493/) (2021)
- [Radar-Camera Pixel Depth Association for Depth Completion](https://mlsanity.com/paper/2106.02778/) (2021)
- [RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization](https://mlsanity.com/paper/2102.05150/) (2021)
- [Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2103.16214/) (2021)
- [AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving](https://mlsanity.com/paper/2302.08646/) (2023)
- [MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty](https://mlsanity.com/paper/2401.12761/) (2024)
- [RING++: Roto-translation Invariant Gram for Global Localization on a Sparse Scan Map](https://mlsanity.com/paper/2210.05984/) (2022)
- [Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection](https://mlsanity.com/paper/2306.01438/) (2023)
- [MODS -- A USV-oriented object detection and obstacle segmentation benchmark](https://mlsanity.com/paper/2105.02359/) (2021)
- [BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation](https://mlsanity.com/paper/2403.11761/) (2024)
- [BVMatch: Lidar-based Place Recognition Using Bird's-eye View Images](https://mlsanity.com/paper/2109.00317/) (2021)
- [HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion](https://mlsanity.com/paper/2304.00932/) (2023)
- [Survey on LiDAR Perception in Adverse Weather Conditions](https://mlsanity.com/paper/2304.06312/) (2023)
- [RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception](https://mlsanity.com/paper/2304.06547/) (2023)
- [Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment](https://mlsanity.com/paper/2105.05207/) (2021)
- [RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systems](https://mlsanity.com/paper/2211.06108/) (2022)
- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [Exploiting Temporal Relations on Radar Perception for Autonomous Driving](https://mlsanity.com/paper/2204.01184/) (2022)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection](https://mlsanity.com/paper/2310.16389/) (2023)
- [Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar](https://mlsanity.com/paper/2405.04662/) (2024)
- [Full-Velocity Radar Returns by Radar-Camera Fusion](https://mlsanity.com/paper/2108.10637/) (2021)
- [Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM](https://mlsanity.com/paper/2309.08086/) (2023)
- [RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes](https://mlsanity.com/paper/2506.01379/) (2025)
- [Line as a Visual Sentence: Context-aware Line Descriptor for Visual Localization](https://mlsanity.com/paper/2109.04753/) (2021)
- [ADCNet: Learning from Raw Radar Data via Distillation](https://mlsanity.com/paper/2303.11420/) (2023)

## Abstract (arXiv)

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. The data were gathered in January 2019 over thirty-two traversals of a central Oxford route spanning a total of 280km of urban driving. It encompasses a variety of weather, traffic, and lighting conditions. This 4.7TB 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-robotcar-dataset