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

By Dan Barnes, Matthew Gadd, P. Murcutt, Paul M. Newman, Ingmar Posner. IEEE International Conference on Robotics and Automation, 2019

463 citations recorded by the source corpus.

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

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8ceee47c407e1e9a9bc736ca18e0f93a13457e38

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Citations in the corpus

Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments (2021, methodology): “In total, eight sequences were selected from four publicly available datasets covering diverse environments: KITTI Odometry [12], MulRan [34], Oxford Radar RobotCar [33], and NAVER LABS 1 datasets.”
Perception and sensing for autonomous vehicles under adverse weather conditions: A survey (2021, methodology): “The radar extension of the Oxford Robotcar dataset [75] used only Navtech radar to accomplish map building and localization in rain and snow, thanks to radar’s specialty in harsh weather.”
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019, methodology): “The Oxford Dataset [73] and the Oxford Radar RobotCar Dataset [84] are collected by driving the car around the Oxford area during the whole year.”
Multi-class Road User Detection with 3+1D Radar in the View-of-Delft Dataset (2022, background): “Recently, several automotive datasets containing radar data were published for various tasks such as localization [35][36], object classiﬁcation [37], or scene understanding with a stationary radar sensor [38].”