用声呐与定位融合实现浑浊浅海高精度海底测绘
Sonar-GPS Fusion for Seabed Mapping in Turbid Shallow Waters with an Autonomous Surface Vehicle
- 结合傅里叶-梅林变换与扩展卡尔曼滤波，融合声呐、GPS、IMU数据
- 相比纯声呐方法，定位误差降低9.5%，实现亚米级精度重建
- 适合浑浊水域的水产养殖区监测，支持牡蛎数量估算
准确的海底测绘对栖息地监测和基础设施检查至关重要。在浑浊的浅海近岸水域（如牡蛎养殖区），传统光学方法效果受限。配备前视声呐（FLS）的自主水面航行器（ASV）提供了可行替代方案。然而，现有基于声呐的系统在长轨迹上难以实现高分辨率映射，主要受限于低分辨率定位测量和长期轨迹累积漂移。本文提出一种抗漂移的海底映射框架，将基于傅里叶-梅林变换（FMT）的局部声呐帧对齐与基于扩展卡尔曼滤波（EKF）的全局轨迹优化相结合，融合全球定位系统（GPS）、惯性测量单元（IMU）和罗盘数据。采用基于方差的图像融合策略，进一步减少重叠区域的视觉伪影。实地试验在结构化的牡蛎养殖场进行，结果表明，该框架使均方根误差（RMSE）相比仅使用FMT的基线降低9.5%。该方法还实现了亚米级重建精度，并保留了用于牡蛎库存估算所需的高分辨率纹理。
原文摘要 · Abstract (English)
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.
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