We describe the release of reference data towards a challenging long-term localisation and mapping benchmark based on the large-scale Oxford RobotCar Dataset. The release includes 72 traversals of a route through Oxford, UK, gathered in all illumination, weather and traffic conditions, and is representative of the conditions an autonomous vehicle would be expected to operate reliably in. Using post-processed raw GPS, IMU, and static GNSS base station recordings, we have produced a globally-consistent centimetre-accurate ground truth for the entire year-long duration of the dataset. Coupled with a planned online benchmarking service, we hope to enable quantitative evaluation and comparison of different localisation and mapping approaches focusing on long-term autonomy for road vehicles in urban environments challenged by changing weather.
@article{arxiv.2002.10152,
title = {Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset},
author = {Will Maddern and Geoffrey Pascoe and Matthew Gadd and Dan Barnes and Brian Yeomans and Paul Newman},
journal= {arXiv preprint arXiv:2002.10152},
year = {2020}
}