Abstract - 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 remains unencumbered by 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 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-robotcar-dataset

Example - In the figure below we show example radar scans in Cartesian form from the dataset at various resolution and ranges. Both the static scene structure (such as buildings, walls and trees) as well as dynamic objects (such as vehicles, cyclists and pedestrians) can be made out at large distances making this modality and interesting candidate sensor for urban robotics.

Raw radar scans in Cartesian form rendered at (from left to right) increasing range's of 25m, 75m and 125m and decreasing resolution. Note: the data has been compressed.

Further Info - For more details please look at the dataset website and the following links or drop me an email:

[Paper] [Dataset Website] [Video] 

@inproceedings{RadarRobotCarDatasetICRA2020,
  address = {Paris},
  author = {Dan Barnes and Matthew Gadd and Paul Murcutt and Paul Newman and Ingmar Posner},
  title = {The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset},
  booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},	
  url = {https://arxiv.org/abs/1909.01300},
  pdf = {https://arxiv.org/pdf/1909.01300.pdf},
  year = {2020}
}