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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

TL;DR

The paper tackles the challenge of creating accurate 3D water quality maps in shallow aquaculture environments by addressing underwater localization without continuous GPS. It introduces an invariant EKF on the Lie group $SE_{2}(3)$ that fuses IMU, DVL and pressure data with periodic GPS resurfacing to estimate pose between corrections, enabling mapping of water quality readings. The authors validate the approach with real-world experiments on a BlueROV2 in the Chesapeake Bay, showing improved localization accuracy over a standard EKF and enabling real-time 3D water-quality mapping using onboard multiparameter sensing. This work provides a practical framework for high-resolution environmental monitoring that can inform aquaculture management and optimization.

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.

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

TL;DR

The paper tackles the challenge of creating accurate 3D water quality maps in shallow aquaculture environments by addressing underwater localization without continuous GPS. It introduces an invariant EKF on the Lie group that fuses IMU, DVL and pressure data with periodic GPS resurfacing to estimate pose between corrections, enabling mapping of water quality readings. The authors validate the approach with real-world experiments on a BlueROV2 in the Chesapeake Bay, showing improved localization accuracy over a standard EKF and enabling real-time 3D water-quality mapping using onboard multiparameter sensing. This work provides a practical framework for high-resolution environmental monitoring that can inform aquaculture management and optimization.

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.
Paper Structure (14 sections, 20 equations, 10 figures, 1 table, 1 algorithm)

This paper contains 14 sections, 20 equations, 10 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Robotics in Aquaculture
  4. Underwater Robot Localization
  5. Methodology
  6. Background and Notations
  7. Proposed Approach Description
  8. IMU Motion Model
  9. Update from DVL Measurement
  10. Update from Pressure Measurement
  11. Setup & Experimental Results
  12. Experiments with BlueROV2
  13. Water Quality Mapping
  14. Conclusion and Future Work

Figures (10)

  • Figure 1: Concept shot of a ROV navigating a field while resurfacing at specific points. The intermediate position is estimated by the Invariant Extended Kalman Filter (InEKF). The water quality is mapped with an onboard multi-parameter sensor. A sample plot for chlorophyll concentration is shown on the bottom left.
  • Figure 2: Process for 3D Water Quality Mapping
  • Figure 3: (left) The BlueROV2 platform with retrofitted GPS mount and DVL. (right) The EXO2 multiparameter sensor is attached to the ROV for water quality analysis.
  • Figure 4: Comparison of Surface GPS reading vs InEKF estimate. The robot was initialized with GPS reading at the start point.
  • Figure 5: Chlorophyll
  • ...and 5 more figures