  Visual place recognition is particularly challenging when places suffer
changes in its appearance. Such changes are indeed common, e.g., due to
weather, night/day or seasons. In this paper we leverage on recent research
using deep networks, and explore how they can be improved by exploiting the
temporal sequence information. Specifically, we propose 3 different
alternatives (Descriptor Grouping, Fusion and Recurrent Descriptors) for deep
networks to use several frames of a sequence. We show that our approaches
produce more compact and best performing descriptors than single- and
multi-view baselines in the literature in two public databases.
