{"ID":2886505,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.03672","arxiv_id":"2508.03672","title":"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","short_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 introduc...","url_abs":"https://arxiv.org/abs/2508.03672","url_pdf":"https://arxiv.org/pdf/2508.03672v2","authors":"[\"Zhongbi Luo\",\"Yunjia Wang\",\"Jan Swevers\",\"Peter Slaets\",\"Herman Bruyninckx\"]","published":"2025-08-05T17:37:43Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}