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.
@article{arxiv.2009.06364,
title = {4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving},
author = {Patrick Wenzel and Rui Wang and Nan Yang and Qing Cheng and Qadeer Khan and Lukas von Stumberg and Niclas Zeller and Daniel Cremers},
journal= {arXiv preprint arXiv:2009.06364},
year = {2025}
}
Comments
German Conference on Pattern Recognition (GCPR 2020)