Seabed-to-Sky Mapping of Maritime Environments with a Dual Orthogonal SONAR and LiDAR Sensor Suite

2025-12-04

AI Summary

Existing marine environmental mapping methods rely on GNSS—prone to signal interference—or expensive multibeam sonar, hindering robust, high-precision, real-time 3D modeling across air–water interfaces. This paper proposes a GNSS-denied autonomous surface vehicle system integrating dual orthogonal forward-looking sonar with a tightly coupled LiDAR–IMU perception architecture. We introduce an innovative orthogonal wide-angle sonar fusion method featuring a frontier-line scanning strategy and extend the LIO-SAM framework to enable persistent integration of sparse, heterogeneous acoustic measurements into the factor graph. Motion-interpolated pose estimation and stereo sonar point cloud reconstruction further enhance geometric consistency. Field experiments in Copenhagen’s Belvederekanalen demonstrate real-time operation: 2.65 Hz map update rate and 2.85 Hz odometry output, yielding a high-fidelity, geometrically consistent 3D environmental model spanning the air–water interface.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Critical maritime infrastructure increasingly demands situational awareness both above and below the surface, yet existing ''seabed-to-sky'' mapping pipelines either rely on GNSS (vulnerable to shadowing/spoofing) or expensive bathymetric sonars. We present a unified, GNSS-independent mapping system that fuses LiDAR-IMU with a dual, orthogonally mounted Forward Looking Sonars (FLS) to generate consistent seabed-to-sky maps from an Autonomous Surface Vehicle. On the acoustic side, we extend orthogonal wide-aperture fusion to handle arbitrary inter-sonar translations (enabling heterogeneous, non-co-located models) and extract a leading edge from each FLS to form line-scans. On the mapping side, we modify LIO-SAM to ingest both stereo-derived 3D sonar points and leading-edge line-scans at and between keyframes via motion-interpolated poses, allowing sparse acoustic updates to contribute continuously to a single factor-graph map. We validate the system on real-world data from Belvederekanalen (Copenhagen), demonstrating real-time operation with approx. 2.65 Hz map updates and approx. 2.85 Hz odometry while producing a unified 3D model that spans air-water domains.

Problem

• Develops a GNSS-free seabed-to-sky mapping system for maritime environments
• Fuses LiDAR-IMU with dual orthogonal sonars on an autonomous surface vehicle
• Generates unified 3D maps integrating both above and below water data

Innovation

• Fuses LiDAR-IMU with dual orthogonal sonars for seabed-to-sky mapping
• Extends orthogonal sonar fusion to handle arbitrary inter-sonar translations
• Modifies LIO-SAM to integrate sparse sonar data into a factor-graph map