融合多模态传感器数据，构建船舶周围鸟瞰图以提升自主航行安全。
Multimodal and Multiview Deep Fusion for Autonomous Marine Navigation
- 基于交叉注意力的变压器架构融合多视角图像与激光雷达点云。
- 结合雷达和电子海图数据训练，实现复杂海况下高精度场景重建。
- 适合自动驾驶船舶、海洋智能感知系统研发人员参考。
我们提出一种基于交叉注意力变压器的多模态传感器融合方法，用于构建船舶周围环境的鸟瞰视图，支持更安全的自主海上导航。该模型深度融合多视角RGB图像、长波红外图像与稀疏激光雷达点云，并在训练中整合X波段雷达和电子海图数据以指导预测。生成的视图提供详细可靠的场景表示，显著提升导航准确性和鲁棒性。真实海况测试验证了该方法在恶劣天气和复杂海域下的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。