@inproceedings{GriesserFranzUmlauf2024,
  author    = {Griesser, Dennis and Franz, Matthias O. and Umlauf, Georg},
  title     = {Enhancing Inland Water Safety: The Lake Constance Obstacle Detection Benchmark},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA), 13-17 May 2024, Yokohama, Japan},
  volume    = {2024},
  isbn      = {979-8-3503-8457-4},
  doi       = {10.1109/ICRA57147.2024.10610600},
  institution = {Institut f{\"u}r Optische Systeme - IOS},
  pages     = {14808 -- 14814},
  year      = {2024},
  abstract  = {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.},
  language  = {en}
}