Dual-Branch Feature Generalization Method for AUV Near-Field Exploration of Hydrothermal Areas
The simultaneous localization and mapping (SLAM) technique provides long-term near-seafloor navigation for autonomous underwater vehicles (AUVs). However, the stability of descriptors generated by interest point detectors remains a challenge in the hydrothermal environment. This paper proposes a dua...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/12/12/2359 |
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| Summary: | The simultaneous localization and mapping (SLAM) technique provides long-term near-seafloor navigation for autonomous underwater vehicles (AUVs). However, the stability of descriptors generated by interest point detectors remains a challenge in the hydrothermal environment. This paper proposes a dual-branch feature generalization method, incorporating volumetric density and color distribution for enhanced robustness. The method utilizes shared descriptors and a feature confidence mechanism, combining neural radiance fields with Gaussian splatting models, ensuring fast and accurate feature generalization. The proposed approach improves recall while maintaining matching accuracy, ensuring stability and robustness in feature matching. This method achieves stable and reliable feature matching in a simulated hydrothermal environment. |
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| ISSN: | 2077-1312 |