  Visual place recognition is a key to unlocking spatial navigation for
animals, humans and robots. While state-of-the-art approaches are trained in a
supervised manner and therefore hardly capture the information needed for
generalizing to unusual conditions, we argue that self-supervised learning may
help abstracting the place representation so that it can be foreseen,
irrespective of the conditions. More precisely, in this paper, we investigate
learning features that are robust to appearance modifications while sensitive
to geometric transformations in a self-supervised manner. This dual-purpose
training is made possible by combining the two self-supervision main paradigms,
\textit{i.e.} contrastive and predictive learning. Our results on standard
benchmarks reveal that jointly learning such appearance-robust and
geometry-sensitive image descriptors leads to competitive visual place
recognition results across adverse seasonal and illumination conditions,
without requiring any human-annotated labels.
