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

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.

Citation

If you find our dataset useful and you make us of it in your research, please cite the following paper:

@inproceedings{wenzel2020fourseasons,
author = {P. Wenzel and R. Wang and N. Yang and Q. Cheng and Q. Khan and L. von Stumberg and N. Zeller and D. Cremers},
title = {{4Seasons}: A Cross-Season Dataset for Multi-Weather {SLAM} in Autonomous Driving},
booktitle = {Proceedings of the German Conference on Pattern Recognition ({GCPR})},
year = {2020}
}