Outdoor drone positioning enhanced with new magnetic field dataset
FlightMagNav: An Open Dataset and Probabilistic Map Learning and Validation Framework for Outdoor Magnetic Field-Based Positioning
Robotics
Summary
Knowing where a flying drone is can be tricky without GPS or other help. The authors created a new openly available dataset using special quantum sensors on drones to measure Earth's magnetic field near the ground. They also developed a way to learn and test maps of these magnetic fields to help figure out the drone's position. This work can help make drones find their way better using only the magnetic environment around them.
What this means in practice
- •For drone developers: Create magnetic field-based positioning to reduce reliance on GPS for drones flying close to the ground.
- •For robotics engineers: Use the dataset and framework to test and improve location methods for autonomous aerial or ground robots in areas without infrastructure.
Authors
Isaac Skog, Miguel Ramos Galrinho, Martin Gelin
Abstract
Magnetic field-based positioning is a resilient positioning technology that requires no external infrastructure and is hard to jam at scale. To facilitate research on magnetic field-based positioning for aerial platforms operating close to the Earth's surface, an open dataset is presented with measurements collected using an unmanned aerial vehicle carrying two optically pumped magnetometers, a type of quantum magnetometer, and a global navigation satellite system-aided inertial navigation system. The dataset includes measurements for both magnetic-field map learning and validation. Along with the dataset, a probabilistic framework for magnetic-field map learning and validation is presented and used to illustrate how the dataset may be used. Finally, we outline research directions that may be explored using the dataset.