Paper: Knowledge Distillation for Feature Extraction in Underwater VSLAM (ICRA 2023) ArXiv or IEEE
UFEN is an underwater feature extraction and matching network.
We use in-air RGBD data to generate synthetic underwater images and employ these as the medium to distil knowledge from a teacher model SuperPoint.
Refer to GCNv2, We embed UFEN into the ORB-SLAM3 framework to replace the ORB feature. The code of UFEN-SLAM will be public shortly.
The feature-matching code in Python has been released below.
The code of UFEN-SLAM will be public shortly.
We also built a new underwater dataset in different water turbidities with groundtruth measurements named EASI. The EASI dataset can be found in EASI Dataset.
Tracking Loss (ORB-SLAM3 VS UFEN-SLAM)
Initialization Failure (ORB-SLAM3 VS UFEN-SLAM)
The fast implementation code of UFEN feature matching is public in UFEN_Demo.
The original weight can be found in SuperPoint.
The weights of UFEN can be downloaded in weights.
(UFEN_v1 is the retrained version from the original paper, while UFEN_v2 is an improved version achieved by fine-tuning the parameters.)
Image pairs are extracted from the EASI Dataset and the real underwater videos.
The code of UFEN-SLAM will be public shortly.
Please cite our papers if you use the EASI dataset or the UFEN.
@INPROCEEDINGS{10161047,
author={Yang, Jinghe and Gong, Mingming and Nair, Girish and Lee, Jung Hoon and Monty, Jason and Pu, Ye},
booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
title={Knowledge Distillation for Feature Extraction in Underwater VSLAM},
year={2023},
doi={10.1109/ICRA48891.2023.10161047}}