  Recently Convolutional Neural Networks (CNNs) have been shown to achieve
state-of-the-art performance on various classification tasks. In this paper, we
present for the first time a place recognition technique based on CNN models,
by combining the powerful features learnt by CNNs with a spatial and sequential
filter. Applying the system to a 70 km benchmark place recognition dataset we
achieve a 75% increase in recall at 100% precision, significantly outperforming
all previous state of the art techniques. We also conduct a comprehensive
performance comparison of the utility of features from all 21 layers for place
recognition, both for the benchmark dataset and for a second dataset with more
significant viewpoint changes.
