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

TL;DR

Inland-LOAM is a novel LiDAR SLAM framework designed for inland waterway navigation that improves localization accuracy and generates real-time semantic maps, including shorelines, to support autonomous waterway transport.

Contribution

It introduces a voxel-based semantic mapping pipeline and water surface constraints to enhance LiDAR SLAM performance in waterway environments.

Findings

Achieves superior localization accuracy compared to state-of-the-art methods.

Generates real-time semantic maps and shorelines aligned with real-world conditions.

Provides reliable data for autonomous 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