3D Water Quality Mapping using Invariant Extended Kalman Filtering for Underwater Robot Localization

Kaustubh Joshi, Tianchen Liu, Alan Williams, Matthew Gray, Xiaomin Lin, Nikhil Chopra
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Abstract

Water quality mapping for critical parameters such as temperature, salinity, and turbidity is crucial for assessing an aquaculture farm's health and yield capacity. Traditional approaches involve using boats or human divers, which are time-constrained and lack depth variability. This work presents an innovative approach to 3D water quality mapping in shallow water environments using a BlueROV2 equipped with GPS and a water quality sensor. This system allows for accurate location correction by resurfacing when errors occur. This study is being conducted at an oyster farm in the Chesapeake Bay, USA, providing a more comprehensive and precise water quality analysis in aquaculture settings.
利用不变扩展卡尔曼滤波进行水下机器人定位的 3D 水质绘图
绘制温度、盐度和浊度等关键参数的水质图对于评估水产养殖场的健康状况和产量能力至关重要。传统方法需要使用船只或人力潜水员,这不仅受时间限制,而且缺乏深度可变性。这项工作提出了一种在浅水环境中使用配备全球定位系统和水质传感器的蓝鲸 2 号潜水器进行三维水质测绘的创新方法。该系统可在出现错误时通过重新浮出水面进行精确定位校正。这项研究正在美国切萨皮克湾的一个牡蛎养殖场进行,为水产养殖环境提供更全面、更精确的水质分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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