Sonar�GPS Fusion for Seabed Mapping in Turbid Shallow Waters with an Autonomous Surface Vehicle
Yisheng Zhang, Michael Xu, Alan Williams, Matthew Gray, Nare Karapetyan, Miao Yu
AI summary
Problem
Traditional optical imaging fails in turbid shallow waters, while existing sonar-based mapping accumulates positional drift over long trajectories, preventing accurate high-resolution seabed mosaics needed for applications like oyster inventory monitoring.
Approach
The method fuses Fourier–Mellin transform-based local sonar alignment with global trajectory optimization from an extended Kalman filter (GPS, IMU, compass), then applies a variance-based blending strategy to suppress artifacts in overlapping frames.
Key results
- 9.5% reduction in drift RMSE over FMT-only baseline
- Sub-meter reconstruction accuracy in field trials
- Variance-based blending suppresses striping while preserving fine seabed textures
- Validated on a low-cost ASV surveying a structured oyster farm
Why it matters
Provides a low-cost, drift-resilient mapping solution for precision aquaculture and marine habitat monitoring in challenging turbid environments.
Abstract
Accurate seabed mapping is essential for habitat monitoring and infrastructure inspection. In turbid, shallow coastal waters, such as shellfish aquaculture farms, the effec- tiveness 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 op- timization 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.