Racetrack Rolling-shutter Stereo Visual Odometry and Dataset

In this paper we present a novel vehicle localization dataset captured with a pair of stereo rolling shutter cameras mounted on top a race car while driving at speeds of more than 200 km/h. The dataset contains six sequences recorded on two different racetrack coupled with accurate ground truth trajectories recorded with a real-time kinematics GNSS system. We make the dataset publicly available to the research community along with raw calibration sequences and our estimated calibration parameters. Besides the dataset, in the paper we also propose an extended version of our previously published stereo odometry, SOFT2, to the case of rolling shutter stereo cameras. The approach successfully models the rolling shutter effect and addresses the challenges both in the front- and back-end part of the odometry due to the line-by-line scanning nature of the sensor during image capture. Specifically, for the former the challenge of feature matching in a texture-poor environment of a racetrack, while for the later the challenge of not having a unique essential matrix for all feature correspondences as in the global shutter case. The proposed odometry is evaluated on the six sequences and achieves accuracy on par with the state-of-the-art methods of the KITTI benchmark.The dataset is available at https://unizgfer-lamor.github.io/rt-dataset.