MOANA：面向海上里程计与自主导航应用的多雷达数据集
MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
AI总结：
本文发布MOANA多雷达海上数据集，融合短距LiDAR、中距W波段与长距X波段雷达及标注，支持里程计、SLAM、检测和动态物体消除研究。
摘要：
海上环境感知需要克服恶劣天气、平台扰动、大型动态物体以及远距离探测需求等复杂条件带来的挑战。尽管相机和LiDAR常用于地面车辆导航，但它们在海上场景中的适用性受到距离限制和硬件维护问题的制约。相比之下，雷达传感器提供稳健的远距离探测能力，并能抵御天气和盐渍环境造成的物理污染，因此成为海上导航的强大传感器。在各类雷达中，X波段雷达被广泛用于海上船舶导航，可提供态势感知和避碰所必需的有效远距离探测。然而，在近场探测至关重要的靠泊作业期间，它存在局限。为弥补这一不足，我们引入了擅长以更高更新率探测附近物体的W波段雷达。我们提出了一个具有多距离探测能力的综合海上传感器数据集。该数据集将短距离LiDAR数据、中距离W波段雷达数据和长距离X波段雷达数据整合到一个统一框架中。此外，它还包含用于海洋目标检测的目标标签，这些标签由雷达和立体相机图像生成。数据集包含从不同区域采集的七个序列，具有从简单到具有挑战性的不同navigation algorithm（导航算法）估计难度，并包括适合全局定位任务的常见地点。该数据集为推进海上环境中的地点识别、里程计估计、SLAM、目标检测和动态物体消除研究提供了宝贵资源。数据集可在 https://sites.google.com/view/rpmmoana 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
