We have published a number of datasets. Under each heading you will find the associated paper, as well as links to their websites where relevant. We also make some data from other publications available as is.

New College Vision and Laser Dataset

FABMAP Multimedia Extension Dataset

FABMAP 10k and 100k word vocabularies can be requested here.

Data used in “Navigating, Recognising and Describing Urban Spaces With Vision and Laser”

(ftp) Alog File For November 7th 2008 (180M)

Note: Alogs can be parsed by the software available on The New College Vision and Laser Dataset (see above). If you are prompted for a username and password, use the following:
User: Anonymous

RobotCar Dataset

The Oxford RobotCar Dataset contains over 100 repetitions of a consistent route through Oxford, UK, captured over a period of over a year. The dataset captures many different combinations of weather, traffic and pedestrians, along with longer term changes such as construction and roadworks.

You can find it here: http://robotcar-dataset.robots.ox.ac.uk/

  • [PDF] [DOI] W. Maddern, G. Pascoe, C. Linegar, and P. Newman, “1 Year, 1000 km: The Oxford RobotCar dataset.” in The International Journal of Robotics Research 2017, vol. 36, p. 3–15, ePrint: http://dx.doi.org/10.1177/0278364916679498, DOI: 10.1177/0278364916679498 (link).
    author = {Maddern, Will and Pascoe, Geoffrey and Linegar, Chris and Newman, Paul},
    title = {1 Year, 1000 km: The Oxford RobotCar dataset},
    journal = {The International Journal of Robotics Research},
    year = {2017},
    volume = {36},
    number = {1},
    pages = {3--15},
    abstract = {We present a challenging new dataset for autonomous driving: the Oxford RobotCar Dataset. Over the period of May 2014 to December 2015 we traversed a route through central Oxford twice a week on average using the Oxford RobotCar platform, an autonomous Nissan LEAF. This resulted in over 1000 km of recorded driving with almost 20 million images collected from 6 cameras mounted to the vehicle, along with LIDAR, GPS and INS ground truth. Data was collected in all weather conditions, including heavy rain, night, direct sunlight and snow. Road and building works over the period of a year significantly changed sections of the route from the beginning to the end of data collection. By frequently traversing the same route over the period of a year we enable research investigating long-term localization and mapping for autonomous vehicles in real-world, dynamic urban environments. The full dataset is available for download at: http://robotcar-dataset.robots.ox.ac.uk},
    doi = {10.1177/0278364916679498},
    eprint = {http://dx.doi.org/10.1177/0278364916679498},
    pdf = {http://robotcar-dataset.robots.ox.ac.uk/images/robotcar_ijrr.pdf},
    url = {http://dx.doi.org/10.1177/0278364916679498},

Radar RobotCar Dataset

The Oxford Radar RobotCar Dataset is 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.

You can find it here: http://ori.ox.ac.uk/datasets/radar-robotcar-dataset

  • [PDF] D. Barnes, M. Gadd, P. Murcutt, P. Newman, and I. Posner, “The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset.” in arXiv preprint arXiv: 1909.01300 2019 (link).
    author = {Barnes, Dan and Gadd, Matthew and Murcutt, Paul and Newman, Paul and Posner, Ingmar},
    title = {The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset},
    journal = {arXiv preprint arXiv: 1909.01300},
    year = {2019},
    pdf = {https://arxiv.org/pdf/1909.01300.pdf},
    url = {https://arxiv.org/pdf/1909.01300},

Vote3D – Example Dataset

An example dataset to run vote3d over to demonstrate train and testing a detector with KITTI data: https://ori.ox.ac.uk/vote3d-example-dataset/