Simultaneous Localization and Mapping (SLAM)
in unstructured planetary environments is a challenging task
for mobile robots due to the appearance and structure of the
environment. In urban and man-made scenarios, individual
objects (e.g. cars, trees or buildings) are easily discernible
and the visual appearance is likely to provide unique cues
for the purpose of localization. Contrarily, planetary scenarios
are often characterized by repetitive structures and ambiguous
terrain features. To provide robust place recognition abilities in
the context of submap-based stereo visual SLAM, we propose
to utilize the gradient of elevation maps generated by Gaussian
Processes (GPs). Visual features computed on GP Gradient
Maps (GPGMaps) provide means for efficient place recognition,
through encoding in Bag-of-Words vectors, and for SE(2)
alignment to establish loop closure constraints in a pose graph.
We evaluate the proposed SLAM system on relevant Moon-like
environments through real data captured on Mt. Etna, Sicily
