UFEN-SLAM

Paper: Knowledge Distillation for Feature Extraction in Underwater VSLAM (ICRA 2023) ArXiv or IEEE

1. Introduction

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.

2. Demo

Tracking Loss (ORB-SLAM3 VS UFEN-SLAM)

Initialization Failure (ORB-SLAM3 VS UFEN-SLAM)

3. UFEN Feature Matching Implementation

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.

4. UFEN-SLAM

The code of UFEN-SLAM will be public shortly.

Citation

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}}