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arXiv · 2009.06364

4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving

Patrick Wenzel·Rui Wang·Nan Yang·Qing Cheng·Qadeer Khan·Lukas von Stumberg·Niclas Zeller·Daniel Cremers
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Abstract

We present a novel dataset covering seasonal and challenging perceptual conditions for autonomous driving. Among others, it enables research on visual odometry, global place recognition, and map-based re-localization tracking. The data was collected in different scenarios and under a wide variety of weather conditions and illuminations, including day and night. This resulted in more than 350 km of recordings in nine different environments ranging from multi-level parking garage over urban (including tunnels) to countryside and highway. We provide globally consistent reference poses with up-to centimeter accuracy obtained from the fusion of direct stereo visual-inertial odometry with RTK-GNSS. The full dataset is available at https://go.vision.in.tum.de/4seasons.

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Patrick Wenzel, Rui Wang, Nan Yang, Qing Cheng, Qadeer Khan, Lukas von Stumberg, Niclas Zeller, Daniel Cremers. 2020-09-14. 4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving. https://arxiv.org/abs/2009.06364

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