发表机构
Technical University of Denmark（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对海洋环境下水面船只自主性研发中的传感器数据采集难题，提出融合多模态传感器的模块化平台，依托ROS2框架实现长时数据采集与可复现性，为自主海洋导航提供标准化数据集基础。
AI 中文摘要
提升水面船只的自主性需要对其感知与感知子系统进行系统性评估，但海洋环境带来了独特挑战：传感器安装受船只布局限制，雾、海杂波等环境条件难以复现，长航时任务使数据采集复杂化。本研究解决的问题是：如何为海洋自主性设计一种模块化且可复现的传感器平台？我们提出了一套综合设计蓝图，融合多种模态——RADAR（雷达）、LiDAR（激光雷达）、IMU（惯性测量单元）、GNSS（全球导航卫星系统）、AIS（自动识别系统）、RGB（红-绿-蓝）与LWIR（长波红外）相机，以及气象传感器，以提升环境感知与船只本体感知。该模块化平台由基于ROS2（机器人操作系统2）的专用软件框架支撑数据管理，支持长时数据采集、硬件在环测试，并可与现有传感器及算法集成。通过统一硬件设计与数据采集方法，该平台提升了不同船只及研究项目间的可复现性与可比性。所提框架搭建了工程实现与研究方法间的桥梁，为推进态势感知与自主海洋导航所需的标准化、可验证数据集奠定了基础。
英文摘要
Advancing autonomy for surface vessels requires systematic evaluation of their sensing and perception subsystems. Yet, maritime environments impose unique challenges: sensor installation is constrained by vessel layout, environmental conditions such as fog or sea clutter are difficult to reproduce, and long-duration missions complicate data collection. This work addresses the question: How can we design a modular and reproducible sensor platform for maritime autonomy? We present a comprehensive design blueprint that incorporates diverse modalities - RADAR, LiDAR, IMU, GNSS, AIS, RGB and LWIR cameras, and weather sensors - to enhance environmental awareness and vessel proprioception. Supported by a dedicated ROS2-based software framework for data management, our modular platform enables long-term data collection, hardware-in-the-loop testing, and integration with existing sensors and algorithms. By unifying hardware design and data capture methodology, the platform enhances reproducibility and comparability across vessels and research projects. The proposed framework bridges engineering implementation and research methodology, providing the foundation for standardized, verifiable datasets essential to advancing situational awareness and autonomous maritime navigation.
CommentsSubmitted and accepted to the IFAC WC 2026 as an invited session paper for track 7.2 Transportation and Vehicle Systems - Marine Systems
