  We introduce a new real-time pipeline for Simultaneous Localization and
Mapping (SLAM) and Visual Inertial Odometry (VIO) in the context of planetary
rovers. We leverage prior information of the location of the lander to propose
an object-level SLAM approach that optimizes pose and shape of the lander
together with camera trajectories of the rover. As a further refinement step,
we propose to use techniques of interpolation between adjacent temporal
samples; videlicet synthesizing non-existing images to improve the overall
accuracy of the system. The experiments are conducted in the context of the
Iris Lunar Rover, a nano-rover that will be deployed in lunar terrain in 2021
as the flagship of Carnegie Mellon, being the first unmanned rover of America
to be on the Moon.
