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.

该研究描述了一项挑战性的长期定位和映射基准数据的发布，基于大规模牛津机器人车数据集。该数据集包括经过英国牛津市的72次通过路线，代表了自动驾驶车辆可靠运行的条件。使用后处理的原始GPS、IMU和静态GNSS基站记录，我们已经为整个数据集的长达一年的持续时间产生了全球一致的厘米级精度的地面真实性。配合计划中的在线基准测试服务，我们希望能够实现在城市环境中长期自主驾驶汽车的不同定位和映射方法的定量评估和比较。

Oxford机器人车数据集的实时动态运动学地面真实性