  The ability to determine the pose of a rover in an inertial frame
autonomously is a crucial capability necessary for the next generation of
surface rover missions on other planetary bodies. Currently, most on-going
rover missions utilize ground-in-the-loop interventions to manually correct for
drift in the pose estimate and this human supervision bottlenecks the distance
over which rovers can operate autonomously and carry out scientific
measurements. In this paper, we present ShadowNav, an autonomous approach for
global localization on the Moon with an emphasis on driving in darkness and at
nighttime. Our approach uses the leading edge of Lunar craters as landmarks and
a particle filtering approach is used to associate detected craters with known
ones on an offboard map. We discuss the key design decisions in developing the
ShadowNav framework for use with a Lunar rover concept equipped with a stereo
camera and an external illumination source. Finally, we demonstrate the
efficacy of our proposed approach in both a Lunar simulation environment and on
data collected during a field test at Cinder Lakes, Arizona.
