4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2020
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| _version_ | 1866912440362467328 |
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| author | Wenzel, Patrick Wang, Rui Yang, Nan Cheng, Qing Khan, Qadeer von Stumberg, Lukas Zeller, Niclas Cremers, Daniel |
| author_facet | Wenzel, Patrick Wang, Rui Yang, Nan Cheng, Qing Khan, Qadeer von Stumberg, Lukas Zeller, Niclas Cremers, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2009_06364 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | 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 Computer Vision and Pattern Recognition 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. |
| title | 4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2009.06364 |