  Simultaneous localization and mapping (SLAM) is a critical capability for any
autonomous underwater vehicle (AUV). However, robust, accurate state estimation
is still a work in progress when using low-cost sensors. We propose enhancing a
typical low-cost sensor package using widely available and often free prior
information; overhead imagery. Given an AUV's sonar image and a partially
overlapping, globally-referenced overhead image, we propose using a
convolutional neural network (CNN) to generate a synthetic overhead image
predicting the above-surface appearance of the sonar image contents. We then
use this synthetic overhead image to register our observations to the provided
global overhead image. Once registered, the transformation is introduced as a
factor into a pose SLAM factor graph. We use a state-of-the-art simulation
environment to perform validation over a series of benchmark trajectories and
quantitatively show the improved accuracy of robot state estimation using the
proposed approach. We also show qualitative outcomes from a real AUV field
deployment. Video attachment: https://youtu.be/_uWljtp58ks
