Single-View Place Recognition under Seasonal Changes

TL;DR

This paper addresses the challenge of seasonal appearance changes in single-view place recognition by proposing dataset partitioning and evaluating neural network architectures, achieving state-of-the-art results.

Contribution

It introduces a new partitioning of the Nordland dataset for seasonal recognition and evaluates multiple neural network architectures for improved accuracy.

Findings

Best neural network models outperform previous methods

Partitioning the dataset improves recognition consistency

Neural networks handle seasonal variability effectively

Abstract

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/.