Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression

Abstract

This work presents a number of techniques to improve the ability to create magnetic field maps on a UAV which can be used to quickly and reliably gather magnetic field observations at multiple altitudes in a workspace. Unfortunately, the electronics on the UAV can introduce their own magnetic fields, distorting the resultant magnetic field map. We show methods of reducing and working with UAV-induced noise to better enable magnetic fields as a sensing modality for indoor navigation. First, some gains in our flight controller create high-frequency motor commands that introduce large noise in the measured magnetic field. Next, we implement a common noise reduction method of distancing the magnetometer from other components on our UAV. Finally, we introduce what we call a compromise GPR (Gaussian process regression) map that can be trained on multiple flight tests to learn any flight-by-flight variations between UAV observation tests. We investigate the spatial density of observations used to train a GPR map then use the compromise map to define a consistency test that can indicate whether or not the magnetometer data and corresponding GPR map are appropriate to use for state estimation. The interventions we introduce in this work facilitate indoor position localization of a UAV whose estimates we found to be quite sensitive to noise generated by the UAV.