Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1909.01300 v3 pith:O677WIIE submitted 2019-09-03 cs.RO eess.SP

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

Dan Barnes , Matthew Gadd , Paul Murcutt , Paul Newman , Ingmar Posner This is my paper
classification cs.RO eess.SP
keywords datasetradaroxfordrobotcarconditionsdatalidarsscans
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original 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 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

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CaRLi-V: Camera-RADAR-LiDAR Point-Wise 3D Velocity Estimation

    cs.RO 2025-11 unverdicted novelty 7.0

    CaRLi-V fuses RADAR velocity cube, camera optical flow, and LiDAR ranges in a closed-form solution to produce dense point-wise 3D velocity estimates that outperform scene flow methods on a custom dataset.