A Visual Cooperative Localization Method for Airborne Magnetic Surveying Based on a Manifold Sensor Fusion Algorithm Using Lie Groups

TL;DR

This paper introduces a visual cooperative localization method for UAV magnetic surveying in GNSS-denied environments, combining visual processing with a manifold sensor fusion algorithm to achieve high-precision positioning.

Contribution

It presents a novel sensor fusion algorithm based on Lie groups integrated with visual data, tailored for magnetic surveying UAVs in challenging environments.

Findings

Achieved centimeter-level accuracy in single-axis positioning

Demonstrated decimeter-level 3D positioning accuracy in real flights

Validated effectiveness through real flight experiments

Abstract

Recent advancements in UAV technology have spurred interest in developing multi-UAV aerial surveying systems for use in confined environments where GNSS signals are blocked or jammed. This paper focuses airborne magnetic surveying scenarios. To obtain clean magnetic measurements reflecting the Earth's magnetic field, the magnetic sensor must be isolated from other electronic devices, creating a significant localization challenge. We propose a visual cooperative localization solution. The solution incorporates a visual processing module and an improved manifold-based sensor fusion algorithm, delivering reliable and accurate positioning information. Real flight experiments validate the approach, demonstrating single-axis centimeter-level accuracy and decimeter-level overall 3D positioning accuracy.