打造可复现的海上感知平台，助力无人船自主导航研究
Seeing above the waves: A modular sensing framework for data acquisition at sea
- 模块化设计整合雷达、激光雷达、摄像头等多源传感器
- 支持长时间数据采集与软硬件在环测试，提升实验可复现性
- 适用于海上无人船研究，推动标准化数据集建设
提升水面航行器的自主性需要对其感知与认知子系统进行系统评估。然而，海洋环境带来独特挑战：传感器安装受船体布局限制，雾天或海面杂波等环境难以复现，长期任务也使数据采集复杂化。本文回答的核心问题是：如何设计一个模块化且可复现的海上传感平台？我们提出一套完整的硬件设计蓝图，集成雷达、激光雷达、惯导、定位、船舶识别、可见光与长波红外相机及气象传感器，以增强环境感知与船体本体感知能力。基于专用ROS2软件框架实现数据管理，该模块化平台支持长期数据采集、软硬件在环测试，并可与现有传感器和算法无缝集成。通过统一硬件设计与数据采集方法，平台显著提升了不同船只与研究项目间的可比性与可复现性。该框架连接工程实现与研究方法，为推进态势感知与自主海上导航提供了标准化、可验证的数据基础。
原文摘要 · Abstract (English)
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.
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