Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset
This provides a benchmark for researchers working on long-term autonomy in urban environments, though it is incremental as it builds on existing data.
The authors tackled the problem of evaluating long-term localization and mapping for autonomous vehicles by releasing a centimeter-accurate ground truth dataset based on the Oxford RobotCar Dataset, enabling quantitative comparisons across varying conditions.
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.