Inland-LOAM: Voxel-Based Structural Semantic Mapping for Inland Waterways

2025-08-05

AI Summary

In inland waterway autonomous navigation, two critical bottlenecks impede reliable operation: (1) the lack of real-time updates in International Electronic Navigational Chart (IENC) data, and (2) vertical drift and non-semantic map generation inherent to conventional LiDAR SLAM in aquatic environments. To address these, this paper proposes a high-precision, semantics-enhanced real-time mapping method. It mitigates drift via water-surface planar geometric constraints and improved feature extraction; introduces a novel voxel-based geometric analysis framework for 3D→2D semantic mapping, enabling real-time navigational parameter estimation and IENC-compliant automatic shoreline extraction; and integrates voxelized point cloud processing, planar optimization, geometric-semantic segmentation, and shoreline generation to construct structured semantic maps. Experimental validation demonstrates superior localization accuracy over state-of-the-art SLAM methods and high fidelity between generated maps and ground-truth shorelines, enabling robust navigational situational awareness. The source code and dataset will be publicly released.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Segmentation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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

Problem

• Addresses vertical drift in LiDAR SLAM for waterways
• Converts 3D point clouds to 2D semantic maps
• Generates IENC-compatible shoreline data autonomously

Innovation

• Improved LiDAR feature extraction for waterways
• Voxel-based geometric analysis for semantic maps
• Automated shoreline extraction in IENC format