---
license: cc-by-nc-sa-4.0
---

The Oxford RobotCar Dataset is an autonomous driving dataset collected between May 2014 and December 2015.  
Between May 2014 and December 2015, we drove a route through Oxford city centre on average twice a week using the Oxford RobotCar platform, an autonomous Nissan LEAF vehicle. As a result, more than 1,000 km of traffic data was recorded, with almost 20 million images collected from six cameras mounted on the car, along with data from LIDAR, GPS, and INS. The data was collected in various weather conditions, including heavy rain, nighttime, direct sunlight, and snow.

This is a specifically pre-processed subset designed for the place recognition task in the OpenPlaceRecognition library.

This is a modification of the original Oxford RobotCar dataset:  
- W. Maddern, G. Pascoe, C. Linegar, and P. Newman, "1 Year, 1000 km: The Oxford RobotCar Dataset," *The International Journal of Robotics Research (IJRR)*, 2016.  
- W. Maddern, G. Pascoe, M. Gadd, D. Barnes, B. Yeomans, and P. Newman, "Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset," *arXiv preprint arXiv:2002.10152*, 2020.

Authorship belongs to Maddern et al. This version is released under the same [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.

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# ❗ Please note

**This dataset is currently not compatible with the `datasets` library.**
The recommended way to download the data is by using the `huggingface_hub` library.
Example code snippet:

```python
from pathlib import Path
from huggingface_hub import snapshot_download

out_dir = Path("/dir/to/save/data")
out_dir.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="OPR-Project/OxfordRobotCar_OpenPlaceRecognition", repo_type="dataset", local_dir=out_dir)
```

For reading and working with the data, we recommend using the OpenPlaceRecognition library: https://github.com/OPR-Project/OpenPlaceRecognition