  Single-view place recognition, that we can define as finding an image that
corresponds to the same place as a given query image, is a key capability for
autonomous navigation and mapping. Although there has been a considerable
amount of research in the topic, the high degree of image variability (with
viewpoint, illumination or occlusions for example) makes it a research
challenge.
  One of the particular challenges, that we address in this work, is weather
variation. Seasonal changes can produce drastic appearance changes, that
classic low-level features do not model properly. Our contributions in this
paper are twofold. First we pre-process and propose a partition for the
Nordland dataset, frequently used for place recognition research without
consensus on the partitions. And second, we evaluate several neural network
architectures such as pre-trained, siamese and triplet for this problem. Our
best results outperform the state of the art of the field. A video showing our
results can be found in https://youtu.be/VrlxsYZoHDM. The partitioned version
of the Nordland dataset at http://webdiis.unizar.es/~jmfacil/pr-nordland/.
