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

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