构建多雷达海洋导航数据集，支持远中近三段式感知。
MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
- 融合X波段（远距）、W波段（近距）雷达与激光雷达，实现多尺度检测。
- 包含7个不同海域序列，覆盖从易到难的导航挑战场景。
- 适合做海上定位、里程计、目标检测与动态障碍物剔除研究。
海上环境感知面临恶劣天气、平台扰动、大尺寸动态物体及长距离探测需求等挑战。尽管相机与激光雷达在地面车辆导航中广泛应用，但在海上受限于测距范围和硬件维护问题。雷达传感器则具备强韧的远距离探测能力，且耐受气象与盐雾侵蚀，是海上导航的理想选择。其中X波段雷达广泛用于船舶导航，提供关键的远距离态势感知与避碰能力；但其在靠泊操作时近场探测能力不足。为此，本文引入高性能的W波段雷达，可实现高刷新率的近距离目标检测。我们构建了一个综合性的海洋传感数据集，集成短程激光雷达、中程W波段雷达与远程X波段雷达数据，形成统一框架，并基于雷达与双目相机图像标注了海洋目标。数据集包含7个来自不同区域的序列，涵盖从易到难的导航算法估计难度，适用于全球定位任务的典型场景。该数据集为推进海上环境下的位姿识别、里程计估计、SLAM、目标检测与动态物体消除研究提供了重要资源。数据下载地址：https://sites.google.com/view/rpmmoana。
原文摘要 · Abstract (English)
Maritime environmental sensing requires overcoming challenges from complex conditions such as harsh weather, platform perturbations, large dynamic objects, and the requirement for long detection ranges. While cameras and LiDAR are commonly used in ground vehicle navigation, their applicability in maritime settings is limited by range constraints and hardware maintenance issues. Radar sensors, however, offer robust long-range detection capabilities and resilience to physical contamination from weather and saline conditions, making it a powerful sensor for maritime navigation. Among various radar types, X-band radar is widely employed for maritime vessel navigation, providing effective long-range detection essential for situational awareness and collision avoidance. Nevertheless, it exhibits limitations during berthing operations where near-field detection is critical. To address this shortcoming, we incorporate W-band radar, which excels in detecting nearby objects with a higher update rate. We present a comprehensive maritime sensor dataset featuring multi-range detection capabilities. This dataset integrates short-range LiDAR data, medium-range W-band radar data, and long-range X-band radar data into a unified framework. Additionally, it includes object labels for oceanic object detection usage, derived from radar and stereo camera images. The dataset comprises seven sequences collected from diverse regions with varying levels of \bl{navigation algorithm} estimation difficulty, ranging from easy to challenging, and includes common locations suitable for global localization tasks. This dataset serves as a valuable resource for advancing research in place recognition, odometry estimation, SLAM, object detection, and dynamic object elimination within maritime environments. Dataset can be found at https://sites.google.com/view/rpmmoana.
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