Accurate global localization is essential for planetary rovers to reach mission goals and mitigate operational risk. For initial exploration missions, it is inappropriate to deploy GPS or build other infrastructure for navigating. One way of determining global position is to use terrain relative navigation (TRN). TRN compares planetary rover-perspective images and 3D models to existing satellite orbital imagery and digital elevation models (DEMs) for absolute positioning. However, TRN is limited by the quality of orbital data and the presence and uniqueness of terrain features. This work presents a novel combination of belief space planning with terrain relative navigation. Additionally, we introduce a new method for increasing the robustness of belief space planning to noisy map data. The new algorithm provides a statistically significant reduction in localization uncertainty when tested on elevation data produced from lunar orbital imagery.