  State-of-the-art visual place recognition performance is currently being
achieved utilizing deep learning based approaches. Despite the recent efforts
in designing lightweight convolutional neural network based models, these can
still be too expensive for the most hardware restricted robot applications.
Low-overhead VPR techniques would not only enable platforms equipped with
low-end, cheap hardware but also reduce computation on more powerful systems,
allowing these resources to be allocated for other navigation tasks. In this
work, our goal is to provide an algorithm of extreme compactness and efficiency
while achieving state-of-the-art robustness to appearance changes and small
point-of-view variations. Our first contribution is DrosoNet, an exceptionally
compact model inspired by the odor processing abilities of the fruit fly,
Drosophyla melanogaster. Our second and main contribution is a voting mechanism
that leverages multiple small and efficient classifiers to achieve more robust
and consistent VPR compared to a single one. We use DrosoNet as the baseline
classifier for the voting mechanism and evaluate our models on five benchmark
datasets, assessing moderate to extreme appearance changes and small to
moderate viewpoint variations. We then compare the proposed algorithms to
state-of-the-art methods, both in terms of precision-recall AUC results and
computational efficiency.
