  In recent years, learning-based feature detection and matching have
outperformed manually-designed methods in in-air cases. However, it is
challenging to learn the features in the underwater scenario due to the absence
of annotated underwater datasets. This paper proposes a cross-modal knowledge
distillation framework for training an underwater feature detection and
matching network (UFEN). In particular, we use in-air RGBD data to generate
synthetic underwater images based on a physical underwater imaging formation
model and employ these as the medium to distil knowledge from a teacher model
SuperPoint pretrained on in-air images. We embed UFEN into the ORB-SLAM3
framework to replace the ORB feature by introducing an additional binarization
layer. To test the effectiveness of our method, we built a new underwater
dataset with groundtruth measurements named EASI
(https://github.com/Jinghe-mel/UFEN-SLAM), recorded in an indoor water tank for
different turbidity levels. The experimental results on the existing dataset
and our new dataset demonstrate the effectiveness of our method.
