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

TL;DR

This work tackles localization for UAV-based airborne magnetic surveying in GNSS-denied environments where isolating the magnetic sensor from electronics complicates accurate positioning. It proposes a visual cooperative localization framework that integrates a visual processing module (detection, tracking, and arc-based fiducial detection) with an improved manifold-based sensor fusion on the Lie group $SE(3)$. Key contributions include the arc-based fiducial detector, robust to occlusions and viewpoint changes, and a fusion algorithm that combines local and global positioning results to provide reliable measurements. Real flight experiments demonstrate centimeter-level accuracy on individual axes and decimeter-level accuracy in 3D, with validated observability and applicability to surveying, cargo transport, and aerial manipulation.

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.