Sonar-GPS Fusion for Seabed Mapping in Turbid Shallow Waters with an Autonomous Surface Vehicle

2026-05-03

AI Summary

This study addresses the challenge of high-precision, long-range seafloor mapping in turbid shallow waters, where conventional optical methods are ineffective and existing sonar systems suffer from trajectory drift. The authors propose a drift-resistant seabed mapping framework that integrates local frame alignment using forward-looking sonar with global trajectory optimization leveraging multi-sensor data (GPS, IMU, and compass). Local registration is achieved via Fourier–Mellin transform (FMT), while an extended Kalman filter refines the global trajectory. Additionally, variance-weighted image fusion is introduced to suppress stitching artifacts. Field tests conducted in an oyster farm demonstrate a 9.5% reduction in trajectory RMSE compared to a baseline FMT-only approach, achieving sub-meter reconstruction accuracy while preserving high-resolution texture—enabling precise oyster stock estimation.

Abstract

Accurate seabed mapping is essential for habitat monitoring and infrastructure inspection. In turbid, shallow coastal waters, such as shellfish aquaculture farms, the effectiveness of traditional optical methods is limited. Autonomous surface vehicles (ASVs) equipped with forward-looking sonar (FLS) offer a promising alternative. However, existing sonar-based systems face challenges in achieving fine resolution mapping over long trajectories due to low-resolution positioning measurements and accumulated drift over long trajectories. In this paper, we present a drift-resilient seabed mapping framework that integrates local FLS frame alignment using the Fourier-Mellin transform (FMT) with global trajectory optimization based on an extended Kalman filter (EKF) that fuses global positioning system (GPS), inertial measurement unit (IMU), and compass data. A variance-based image blending strategy is used to further reduce visual artifacts in overlapping regions. Field trials on a structured oyster farm site show that our framework helps reduce drift in RMSE by 9.5% relative to the FMT-only baseline. This framework also enables sub-meter reconstruction accuracy and preservation of high-resolution textures needed for oyster inventory estimation within the mapped areas.

Problem

• seabed mapping
• turbid shallow waters
• sonar-GPS fusion
• trajectory drift
• autonomous surface vehicle

Innovation

• Sonar-GPS fusion
• Fourier-Mellin transform
• Extended Kalman filter
• Drift-resilient mapping
• Autonomous surface vehicle