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Main Authors: Dagdilelis, Dimitrios, Grigoriadis, Panagiotis, Galeazzi, Roberto
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2505.01615
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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