Single-View Place Recognition under Seasonal Changes
This addresses a key challenge for autonomous navigation systems by improving robustness to weather variations, though it is incremental as it builds on existing methods.
The paper tackles the problem of single-view place recognition under seasonal changes by evaluating neural network architectures on the Nordland dataset, achieving results that outperform the state of the art.
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/.