  Accurate and complete terrain maps enhance the awareness of autonomous robots
and enable safe and optimal path planning. Rocks and topography often create
occlusions and lead to missing elevation information in the Digital Elevation
Map (DEM). Currently, these occluded areas are either fully avoided during
motion planning or the missing values in the elevation map are filled-in using
traditional interpolation, diffusion or patch-matching techniques. These
methods cannot leverage the high-level terrain characteristics and the
geometric constraints of line of sight we humans use intuitively to predict
occluded areas. We introduce a self-supervised learning approach capable of
training on real-world data without a need for ground-truth information to
reconstruct the occluded areas in the DEMs. We accomplish this by adding
artificial occlusion to the incomplete elevation maps constructed on a real
robot by performing ray casting. We first evaluate a supervised learning
approach on synthetic data for which we have the full ground-truth available
and subsequently move to several real-world datasets. These real-world datasets
were recorded during exploration of both structured and unstructured terrain
with a legged robot, and additionally in a planetary scenario on Lunar analogue
terrain. We state a significant improvement compared to the baseline methods
both on synthetic terrain and for the real-world datasets. Our neural network
is able to run in real-time on both CPU and GPU with suitable sampling rates
for autonomous ground robots. We motivate the applicability of reconstructing
occlusion in elevation maps with preliminary motion planning experiments.
