4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
This dataset enables research on visual odometry and place recognition under varied conditions, but it is incremental as it builds on existing data collection efforts.
The authors introduced the 4Seasons dataset to address the lack of seasonal and weather-diverse data for autonomous driving SLAM, providing over 350 km of recordings with centimeter-accurate reference poses.
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.