发表机构
KU Leuven; TU Eindhoven（鲁汶大学; 埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对内河航道自主水面车辆中LiDAR建图缺乏水面表示的问题，提出HydroMap框架，利用立体观测和概率融合重建水面高程，并与结构地图结合生成2.5D语义地图，在实测数据上高程误差低于5厘米。
AI 中文摘要
在内河航道中运行的自主水面车辆需要对周围结构和水面进行持久表示。基于LiDAR的同步定位与建图通常会产生稀疏或缺失的水面回波，导致该作业面在重建场景中缺失。我们提出HydroMap，一种与里程计解耦的框架，该框架从立体观测中重建水面高程，并将其与结构地图集成。每帧水面点与传播的立体和位姿不确定性形成联合单元观测，连续观测被融合到持久的概率高程图中。随后，语义地图转换将高程图与结构几何结合，形成水、边界、结构和头顶区域的统一2.5D表示。在浦项运河和鲁汶瓦尔特数据集上，相对于同一地图坐标系中表达的LiDAR参考，高程均方根误差保持在5厘米以下。高程图和语义图分别以2赫兹和1赫兹的频率发布。因此，HydroMap通过水面的持久表示补充了LiDAR地图，用于内河航道的下游导航。
英文摘要
Autonomous surface vehicles operating in inland waterways require a persistent representation of both surrounding structures and the water surface. LiDAR-based simultaneous localization and mapping often produces sparse or missing water returns, leaving this operational surface absent from the reconstructed scene. We propose HydroMap, an odometry-decoupled framework that reconstructs water surface elevation from stereo observations and integrates it with the structural map. Per-frame water points form joint cell observations with propagated stereo and pose uncertainty, and successive observations are fused into a persistent probabilistic elevation map. Semantic map conversion then combines the elevation map with structural geometry in a unified 2.5D representation of water, boundaries, structures, and overhead regions. On the Pohang Canal and Leuven Vaart datasets, the elevation RMSE remains below 5 cm relative to LiDAR references expressed in the same map frame. The elevation and semantic maps are published at 2 Hz and 1 Hz, respectively. HydroMap thereby complements LiDAR maps with a persistent representation of the water surface for downstream navigation in inland waterways.
Commentssubmitted to the IEEE
