Inland-LOAM: Voxel-Based Structural Semantic LiDAR Odometry and Mapping for Inland Waterway Navigation
Abstract
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
Keywords
Cite
@article{arxiv.2508.03672,
title = {Inland-LOAM: Voxel-Based Structural Semantic LiDAR Odometry and Mapping for Inland Waterway Navigation},
author = {Zhongbi Luo and Yunjia Wang and Jan Swevers and Peter Slaets and Herman Bruyninckx},
journal= {arXiv preprint arXiv:2508.03672},
year = {2025}
}