Oxford RobotCarデータセット向けリアルタイムキネマティック高精度真値
Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset
大規模Oxford RobotCarデータセットに、GPS・IMU・GNSS基準局の後処理により年間を通じたセンチメートル精度の全球整合な真値を付与し、長期自律走行の位置推定・地図構築ベンチマークを可能にした。
著者: Will Maddern, Geoffrey Pascoe, Matthew Gadd, Dan Barnes, Brian Yeomans, Paul Newman
分類: cs.RO, cs.CV
原文アブストラクト
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.
