Knowledge Distillation for Feature Extraction in Underwater VSLAM

• URL: http://arxiv.org/abs/2303.17981v1
• Date: Fri, 31 Mar 2023 11:33:21 GMT
• Title: Knowledge Distillation for Feature Extraction in Underwater VSLAM
• Authors: Jinghe Yang, Mingming Gong, Girish Nair, Jung Hoon Lee, Jason Monty, Ye Pu
• Abstract summary: 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. To test the effectiveness of our method, we built a new underwater dataset with groundtruth measurements named EASI.
• Score: 29.167521895895455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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