论文标题
牛津机器人数据集的实时运动基础真相
Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset
论文作者
论文摘要
我们描述了基于大规模的牛津机器人数据集的挑战性长期定位和映射基准测试的参考数据的释放。 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.再加上计划的在线基准测试服务，我们希望能够对不同的本地化和映射方法进行定量评估和比较，重点是在不断变化的天气中挑战的城市环境中的公路车辆长期自治。
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.
