4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving - Wenzel, Patrick; Wang, Rui; Yang, Nan; Cheng, Qing; Khan, Qadeer; von Stumberg, Lukas; Zeller, Niclas; Cremers, Daniel (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021)
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://www.4seasons-dataset.com.
CRIS-Team Scopus Importer:2023-03-08
Keywords: Autonomous driving; Long-term localization; SLAM; Visual learning; Visual odometry