发表机构
Brigham Young University（杨百翰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出两种利用海岸几何的自主水面船定位框架，经多数据集验证，LiDAR管线提升轨迹精度，单目架构控制长期漂移，且零样本基础模型可可靠提取海岸线观测，为无GPS海上定位提供新方案。
AI 中文摘要
海岸环境包含丰富且大多未被利用的几何结构，可提供全局参考的定位线索。本研究提出两种互补的定位框架，利用海岸线与水面几何实现无GPS环境下的自主水面船定位。第一种框架利用LiDAR对水面的观测来估计横滚、俯仰和升沉（垂直运动），同时通过海岸线观测与卫星生成的海岸线地图直接配准来恢复全局位置与航向。第二种框架仅依赖被动图像，通过语义分割检测海岸线与地平线。利用提出的海岸场景几何，从单目图像推断海岸线距离，将海岸线观测累积为短时长局部子图，与同一卫星生成的海岸线地图配准，并在分层因子图中融合。在三个真实海岸数据集上评估，LiDAR管线相比标准基线持续提升轨迹精度，而单目架构保持有限的长期漂移。此外，证实现代零样本基础模型可在多样海岸环境中可靠提取海岸线观测。这些结果共同表明，海岸几何为无GPS的海上定位提供强大且可靠的全局参考信息源。
英文摘要
Coastal environments contain rich, largely unexploited geometric structure capable of providing globally referenced localization cues. In this work, we present two complementary localization frameworks that exploit shoreline and water-surface geometry for GPS-denied autonomous surface vessel localization. The first framework leverages LiDAR observations of the water surface to estimate roll, pitch, and heave (vertical motion), while recovering global position and heading through direct registration of shoreline observations against a satellite-derived coastline map. The second framework relies solely on passive imagery to detect the shoreline and horizon through semantic segmentation. Using the proposed coastal scene geometry, shoreline distance is inferred from monocular imagery. Shoreline observations are accumulated into short-duration local submaps, registered against the same satellite-derived coastline map, and fused within a hierarchical factor graph. Evaluated across three real-world coastal datasets, the LiDAR pipeline consistently improves trajectory accuracy over standard baselines, while the monocular architecture maintains bounded long-term drift. In addition, we establish that modern zero-shot foundation models can reliably extract shoreline observations across diverse coastal environments. Together, these results demonstrate that coastal geometry provides a powerful and dependable source of globally referenced information for GPS-denied maritime localization.
Comments22 pages, 13 figures, 7 tables
