Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset
- URL: http://arxiv.org/abs/2002.10152v1
- Date: Mon, 24 Feb 2020 10:34:31 GMT
- Title: Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset
- Authors: Will Maddern, Geoffrey Pascoe, Matthew Gadd, Dan Barnes, Brian
Yeomans, Paul Newman
- Abstract summary: We release reference data towards a challenging long-term localisation and mapping benchmark based on the large-scale Oxford RobotCar dataset.
We have produced a globally-consistent centimetre-accurate ground truth for the entire year-long duration of the dataset.
- Score: 23.75606166843614
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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