| dc.description.abstract | Unmanned underwater vehicles (UUV) are important for industry, science and explora-
tion. A key step in increasing their autonomy and lowering costs is performing 6D pose
estimation on objects it interacts with. This report details pose estimation methods and
the process of creating a dataset for training neural networks. Footage captured in the
sea using a ROV is annotated with segmentation masks and poses. This is made public
along with the tools developed to create it. The dataset appears to be unique in its kind,
similar datasets were made in pools and also not published. Further, the dataset is used
to train Mask R-CNN for 2D object detection. | en_US |
