针对内河航道的激光雷达导航，实现高精度定位与语义地图构建。
Inland-LOAM: Voxel-Based Structural Semantic LiDAR Odometry and Mapping for Inland Waterway Navigation
- 基于体素分析与水面平面约束，抑制垂直漂移。
- 实时生成带语义的2D航道地图，支持桥下净空等参数计算。
- 自动提取岸线并输出兼容IENC标准的轻量格式，适合实际应用。
精确的地理空间信息对安全、自主的内河航运（IWT）至关重要，因现有海图（IENC）缺乏实时细节，传统激光雷达SLAM在水道环境中表现不佳，导致垂直漂移和非语义地图，阻碍自主航行。本文提出Inland-LOAM，一种面向内河航道的激光雷达SLAM框架。通过改进特征提取与水面平面约束，有效缓解垂直漂移问题。设计新型流水线，利用体素化几何分析将3D点云转化为结构化2D语义地图，实现实时计算桥梁净空等导航参数。开发自动化模块，提取岸线并导出为轻量级、兼容IENC格式的数据。在真实数据集上的评估表明，Inland-LOAM在定位精度上优于现有先进方法。生成的语义地图与岸线均与实际环境一致，为增强态势感知提供可靠数据。代码与数据集将公开可用。
原文摘要 · Abstract (English)
Accurate geospatial information is crucial for safe, autonomous Inland Waterway Transport (IWT), as existing charts (IENC) lack real-time detail and conventional LiDAR SLAM fails in waterway environments. These challenges lead to vertical drift and non-semantic maps, hindering autonomous navigation. This paper introduces Inland-LOAM, a LiDAR SLAM framework for waterways. It uses an improved feature extraction and a water surface planar constraint to mitigate vertical drift. A novel pipeline transforms 3D point clouds into structured 2D semantic maps using voxel-based geometric analysis, enabling real-time computation of navigational parameters like bridge clearances. An automated module extracts shorelines and exports them into a lightweight, IENC-compatible format. Evaluations on a real-world dataset show Inland-LOAM achieves superior localization accuracy over state-of-the-art methods. The generated semantic maps and shorelines align with real-world conditions, providing reliable data for enhanced situational awareness. The code and dataset will be publicly available
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