Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset

TL;DR

This paper presents a comprehensive, centimeter-accurate ground truth dataset for the Oxford RobotCar, enabling long-term autonomous vehicle localization benchmarking across diverse conditions.

Contribution

It releases a large-scale, long-term, and highly accurate ground truth dataset for the Oxford RobotCar, supporting evaluation of autonomous vehicle localization methods.

Findings

Provides 72 traversals under varied conditions

Achieves centimeter-level accuracy with post-processed data

Supports long-term autonomous vehicle localization research

Abstract

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.