Enhancing Inland Water Safety: The Lake Constance Obstacle Detection Benchmark
- Autonomous navigation on inland waters requires an accurate understanding of the environment in order to react to possible obstacles. Deep learning is a promising technique to detect obstacles robustly. However, supervised deep learning models require large data-sets to adjust their weights and to generalize to unseen data. Therefore, we equipped our research vessel with a laser scanner and a stereo camera to record a novel obstacle detection data-set for inland waters. We annotated 1974 stereo images and lidar point clouds with 3d bounding boxes. Furthermore, we provide an initial approach and a suitable metric to compare the results on the test data-set. The data-set is publicly available and seeks to make a contribution towards increasing the safety on inland waters.
| Author: | Dennis GriesserORCiD, Matthias O. FranzORCiDGND, Georg UmlaufORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1109/ICRA57147.2024.10610600 |
| ISBN: | 979-8-3503-8457-4 |
| ISBN: | 979-8-3503-8458-1 |
| Parent Title (English): | IEEE International Conference on Robotics and Automation (ICRA), 13-17 May 2024, Yokohama, Japan |
| Volume: | 2024 |
| Publisher: | IEEE |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Publication: | 2024 |
| Release Date: | 2024/08/26 |
| Tag: | Data-set; Stereo; Lidar; Multi sensor system |
| Page Number: | 7 |
| First Page: | 14808 |
| Last Page: | 14814 |
| Note: | Supplemental Items |
| Institutes: | Institut für Optische Systeme - IOS |
| Relevance: | Peer reviewed nach anderen Listungen (mit Nachweis zum Peer Review Verfahren) |
| Open Access?: | Nein |
| Licence (German): |  Urheberrechtlich geschützt |
