  Terrain relative navigation can improve the precision of a spacecraft's
position estimate by detecting global features that act as supplementary
measurements to correct for drift in the inertial navigation system. This paper
presents a system that uses a convolutional neural network (CNN) and image
processing methods to track the location of a simulated spacecraft with an
extended Kalman filter (EKF). The CNN, called LunaNet, visually detects craters
in the simulated camera frame and those detections are matched to known lunar
craters in the region of the current estimated spacecraft position. These
matched craters are treated as features that are tracked using the EKF. LunaNet
enables more reliable position tracking over a simulated trajectory due to its
greater robustness to changes in image brightness and more repeatable crater
detections from frame to frame throughout a trajectory. LunaNet combined with
an EKF produces a decrease of 60% in the average final position estimation
error and a decrease of 25% in average final velocity estimation error compared
to an EKF using an image processing-based crater detection method when tested
on trajectories using images of standard brightness.
