Inland-LOAM: Voxel-Based Structural Semantic LiDAR Odometry and Mapping for Inland Waterway Navigation

Abstract--Accurate and up-to-date geospatial information is crucial for enhancing the safety and autonomy of Inland Waterway Transport (IWT). These challenges lead to significant localization drift and produce point cloud maps lacking the semantic richness required for autonomous decision-making. This paper introduces a comprehensive LiDAR odometry and Mapping framework for inland waterway navigation (Inland-LOAM). We present an improved feature extraction method adapted to unique waterway geometries, combined with a joint optimization that incorporates the water surface as a global planar constraint to mitigate drift. We also propose an innovative pipeline that transforms dense 3D point cloud outputs into structured 2D semantic maps. By constructing semantic voxel grids and performing geometric analyses (roughness, planarity, and slope), our system classifies the environment into meaningful structural categories and supports real-time computation of critical parameters like vertical bridge clearances. An automated module then efficiently extracts shoreline boundaries, exporting them into a lightweight, IENC-compatible format. Extensive evaluations on a diverse, real-world dataset demonstrate that Inland-LOAM achieves superior localization accuracy over state-of-the-art methods. The generated maps and shorelines align with real-world conditions, providing reliable information to enhance navigational situational awareness. Both the dataset and the algorithm are publicly available to support future research. IWT constitutes an essential component of Europe's freight infrastructure, spanning a network exceeding 41,000 km, interlinking major cities and industrial hubs across 13 interconnected Member States [1]. As efforts increase to shift freight from congested road and rail networks, the importance of accurate geospatial information and detailed environmental models for managing and navigating these waterways grows [2]. Zhongbi Luo, Peter Slaets, Jan Swevers and Herman Bruyninckx are with the Division of Robotics, Automation and Mechatronics in the Department of Mechanical Engineering, KU Leuven, 3001 Leu-ven, Belgium (e-mail: zhongbi.luo@kuleuven.be;