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

In this work, we have used the Nordland railroad videos. In 2012, the Norway broadcasting company (NRK) made a documentary about the Nordland Railway, a railway line between the cities of Trondheim and Bodø. They filmed the 729km journey with a camera in the front part of the train in winter, spring, fall and summer. The length of each video is about 10 hours and each frame is timestamped with the GPS coordinates.
This dataset has been used by other research groups in place recognition, for example Gomez-Ojeda et.al.. Each group uses different partitions for training and test, making difficult to reproduce the results. In this work we propose a specific partition of the dataset and a baseline, to guarantee a fair comparison between algorithms.

This figure illustrates the partition of the whole image set in the Nordland dataset. We decided to create the test set with three different sequences of 1,150 images (a total of 3,450, in yellow in the figure). The rest of the images were used for training (24,569, in red in the figure). By using multiple sections, the variety of places and appearance changes contained in the test set increases. We also left a separation of a few kilometers between each test and train section by discarding some images in order to guarantee the difference between test and train data.

Given the similarity between consecutive images, in this work we propose to consider that two images are of the same place if temporally they are separated by 3 images or less. We applied a sliding window of 5 images over the whole dataset in order to group images taken from five consecutive seconds. This process can be seen in the following image.

We released the Caffe based implementation on the github page. Try our code!

@inproceedings{olid2018single,
    Author = {Olid, Daniel and Fácil, José M. and Civera, Javier},
    Title = {Single-View Place Recognition under Seasonal Changes},
    Booktitle = {PPNIV Workshop at IROS 2018},
    Year = {2018}
}