Multimodal and Multiview Deep Fusion for Autonomous Marine Navigation
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913817009586176 |
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| author | Dagdilelis, Dimitrios Grigoriadis, Panagiotis Galeazzi, Roberto |
| author_facet | Dagdilelis, Dimitrios Grigoriadis, Panagiotis Galeazzi, Roberto |
| contents | We propose a cross attention transformer based method for multimodal sensor fusion to build a birds eye view of a vessels surroundings supporting safer autonomous marine navigation. The model deeply fuses multiview RGB and long wave infrared images with sparse LiDAR point clouds. Training also integrates X band radar and electronic chart data to inform predictions. The resulting view provides a detailed reliable scene representation improving navigational accuracy and robustness. Real world sea trials confirm the methods effectiveness even in adverse weather and complex maritime settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01615 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal and Multiview Deep Fusion for Autonomous Marine Navigation Dagdilelis, Dimitrios Grigoriadis, Panagiotis Galeazzi, Roberto Computer Vision and Pattern Recognition Artificial Intelligence We propose a cross attention transformer based method for multimodal sensor fusion to build a birds eye view of a vessels surroundings supporting safer autonomous marine navigation. The model deeply fuses multiview RGB and long wave infrared images with sparse LiDAR point clouds. Training also integrates X band radar and electronic chart data to inform predictions. The resulting view provides a detailed reliable scene representation improving navigational accuracy and robustness. Real world sea trials confirm the methods effectiveness even in adverse weather and complex maritime settings. |
| title | Multimodal and Multiview Deep Fusion for Autonomous Marine Navigation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2505.01615 |