Simultaneous Localization and Mapping (SLAM) techniques play a key role towards long-
term autonomy of mobile robots due to the ability to correct localization errors and produce
consistent maps of an environment over time. Contrarily to urban or man-made
environments, where the presence of unique objects and structures offer unique cues for
localization, the appearance of unstructured natural environments is often ambiguous and
self-similar, hindering the performances of loop closure detection. In this paper, we present …

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 (eg 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 …