TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Griesser, Dennis
A1  - Franz, Matthias O.
A1  - Umlauf, Georg
T1  - Enhancing Inland Water Safety: The Lake Constance Obstacle Detection Benchmark
T2  - IEEE International Conference on Robotics and Automation (ICRA),  13-17 May 2024, Yokohama, Japan
N2  - 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.
KW  - Data-set
KW  - Stereo
KW  - Lidar
KW  - Multi sensor system
Y1  - 2024
SN  - 979-8-3503-8457-4
SB  - 979-8-3503-8457-4
SN  - 979-8-3503-8458-1
SB  - 979-8-3503-8458-1
U6  - https://doi.org/10.1109/ICRA57147.2024.10610600
DO  - https://doi.org/10.1109/ICRA57147.2024.10610600
N1  - Supplemental Items
VL  - 2024
SP  - 14808
EP  - 14814
S1  - 7
PB  - IEEE
ER  -