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

TL;DR

The paper tackles GNSS-denied maritime mapping by presenting a unified seabed-to-sky pipeline that fuses LiDAR-IMU data with a dual orthogonal forward-looking sonar array. It extends orthogonal FLS fusion to handle non-co-located sensors and introduces leading-edge line-scans, integrating acoustic data into a modified LIO-SAM back-end with pose interpolation for different update rates. Key contributions include the extended FLS fusion framework, leading-edge constraints, and real-world validation in Copenhagen demonstrating near real-time map updates. The work offers a practical path toward robust, GNSS-independent cross-domain mapping for coastal surveillance and critical underwater infrastructure.

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.

Figures (11)

• Figure 1: The obtained "seabed-to-sky" map from data collected in Belvederekanalen, Sydhavnen, Copenhagen on the 5th of December 2024.
• Figure 2: Visualization of the dual SONAR mapping setup. (a) shows the overlap region where 3D point clouds are reconstructed. (b) illustrates the orthogonal SONAR configuration used to capture both horizontal and vertical perspectives.
• Figure 3: Hardware setup on the Maritime Robotics Otter USV. The dual SONAR setup is pitched $45^{\circ}$ downwards. The reference frames for the sensors are presented in the panel.
• Figure 4: The SONAR projection geometric model. Adapted from liu_target_2024, Fig. 7, with minor modifications.
• Figure 5: The full mapping pipeline combining both SONARs with the LiDAR+IMU. Leading-edge detection and stereo matching from the SONARs are integrated, and the resulting 3D point cloud is added to the LiDAR map.