Visual location of aerial vehicles using dynamic aleatoric uncertainty

A system for localizing an aerial vehicle in real time using dynamic uncertainty estimates, the system comprising:

• a non-transitory computer readable medium comprising instructions;
• and at least one electronic processor that executes the instructions to perform operations comprising:
• obtaining a terrain image captured by the aerial vehicle;
• passing the terrain image captured by the aerial vehicle to a trained evidential deep learning neural network subsystem, from which a dynamic uncertainty value and a first feature vector are obtained in real time, wherein the trained evidential deep learning neural network subsystem is trained to provide feature vectors and associated dynamic uncertainty values corresponding to input images;
• for each of a plurality of candidate terrain locations, comparing the first feature vector to a respective second feature vector representative of a candidate terrain location of the plurality of candidate terrain locations, from which a respective similarity score is obtained, whereby each candidate terrain location of the plurality of candidate terrain locations is associated with a respective similarity score in real time;
• for at least one of the plurality of candidate terrain locations, updating in real time, by a recursive Bayesian estimator, a respective location weight based at least on the dynamic uncertainty value and the respective similarity score, from which a plurality of location weights are obtained;
• estimating, in real time, a location of the aerial vehicle based on the plurality of location weights;
• and
• providing the location of the aerial vehicle.