Robust Forward-Looking Sonar-Image Mosaicking Without External Sensors for Autonomous Deep-Sea Mining
With the increasing significance of deep-sea resource development, Forward-Looking Sonar (FLS) has become an essential technology for real-time environmental mapping and navigation in deep-sea mining vehicles (DSMV). However, FLS images often suffer from a limited field of view, uneven imaging, and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/7/1291 |
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| author | Xinran Liu Jianmin Yang Changyu Lu Enhua Zhang Wenhao Xu |
| author_facet | Xinran Liu Jianmin Yang Changyu Lu Enhua Zhang Wenhao Xu |
| author_sort | Xinran Liu |
| collection | DOAJ |
| description | With the increasing significance of deep-sea resource development, Forward-Looking Sonar (FLS) has become an essential technology for real-time environmental mapping and navigation in deep-sea mining vehicles (DSMV). However, FLS images often suffer from a limited field of view, uneven imaging, and complex noise sources, making single-frame images insufficient for providing continuous and complete environmental awareness. Existing mosaicking methods typically rely on external sensors or controlled laboratory conditions, often failing to account for the high levels of uncertainty and error inherent in real deep-sea environments. Consequently, their performance during sea trials tends to be unsatisfactory. To address these challenges, this study introduces a robust FLS image mosaicking framework that functions without additional sensor input. The framework explicitly models the noise characteristics of sonar images captured in deep-sea environments and integrates bidirectional cyclic consistency filtering with a soft-weighted feature refinement strategy during the feature-matching stage. For image fusion, a radial adaptive fusion algorithm with a protective frame is proposed to improve edge transitions and preserve structural consistency in the resulting panoramic image. The experimental results demonstrate that the proposed framework achieves high robustness and accuracy under real deep-sea conditions, effectively supporting DSMV tasks such as path planning, obstacle avoidance, and simultaneous localization and mapping (SLAM), thus enabling reliable perceptual capabilities for intelligent underwater operations. |
| format | Article |
| id | doaj-art-711beca58c564f388ff7b4fdef3d3c33 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-711beca58c564f388ff7b4fdef3d3c332025-08-20T03:08:01ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-06-01137129110.3390/jmse13071291Robust Forward-Looking Sonar-Image Mosaicking Without External Sensors for Autonomous Deep-Sea MiningXinran Liu0Jianmin Yang1Changyu Lu2Enhua Zhang3Wenhao Xu4State Key Laboratory of Ocean Engineering (SKLOE), Shanghai Jiao Tong University (SJTU), Shanghai 200240, ChinaState Key Laboratory of Ocean Engineering (SKLOE), Shanghai Jiao Tong University (SJTU), Shanghai 200240, ChinaState Key Laboratory of Ocean Engineering (SKLOE), Shanghai Jiao Tong University (SJTU), Shanghai 200240, ChinaState Key Laboratory of Ocean Engineering (SKLOE), Shanghai Jiao Tong University (SJTU), Shanghai 200240, ChinaState Key Laboratory of Ocean Engineering (SKLOE), Shanghai Jiao Tong University (SJTU), Shanghai 200240, ChinaWith the increasing significance of deep-sea resource development, Forward-Looking Sonar (FLS) has become an essential technology for real-time environmental mapping and navigation in deep-sea mining vehicles (DSMV). However, FLS images often suffer from a limited field of view, uneven imaging, and complex noise sources, making single-frame images insufficient for providing continuous and complete environmental awareness. Existing mosaicking methods typically rely on external sensors or controlled laboratory conditions, often failing to account for the high levels of uncertainty and error inherent in real deep-sea environments. Consequently, their performance during sea trials tends to be unsatisfactory. To address these challenges, this study introduces a robust FLS image mosaicking framework that functions without additional sensor input. The framework explicitly models the noise characteristics of sonar images captured in deep-sea environments and integrates bidirectional cyclic consistency filtering with a soft-weighted feature refinement strategy during the feature-matching stage. For image fusion, a radial adaptive fusion algorithm with a protective frame is proposed to improve edge transitions and preserve structural consistency in the resulting panoramic image. The experimental results demonstrate that the proposed framework achieves high robustness and accuracy under real deep-sea conditions, effectively supporting DSMV tasks such as path planning, obstacle avoidance, and simultaneous localization and mapping (SLAM), thus enabling reliable perceptual capabilities for intelligent underwater operations.https://www.mdpi.com/2077-1312/13/7/1291Forward-Looking Sonarimage mosaickingdeep-sea mining |
| spellingShingle | Xinran Liu Jianmin Yang Changyu Lu Enhua Zhang Wenhao Xu Robust Forward-Looking Sonar-Image Mosaicking Without External Sensors for Autonomous Deep-Sea Mining Journal of Marine Science and Engineering Forward-Looking Sonar image mosaicking deep-sea mining |
| title | Robust Forward-Looking Sonar-Image Mosaicking Without External Sensors for Autonomous Deep-Sea Mining |
| title_full | Robust Forward-Looking Sonar-Image Mosaicking Without External Sensors for Autonomous Deep-Sea Mining |
| title_fullStr | Robust Forward-Looking Sonar-Image Mosaicking Without External Sensors for Autonomous Deep-Sea Mining |
| title_full_unstemmed | Robust Forward-Looking Sonar-Image Mosaicking Without External Sensors for Autonomous Deep-Sea Mining |
| title_short | Robust Forward-Looking Sonar-Image Mosaicking Without External Sensors for Autonomous Deep-Sea Mining |
| title_sort | robust forward looking sonar image mosaicking without external sensors for autonomous deep sea mining |
| topic | Forward-Looking Sonar image mosaicking deep-sea mining |
| url | https://www.mdpi.com/2077-1312/13/7/1291 |
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