A Compact Neural Architecture for Visual Place Recognition
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State-of-the-art algorithms for visual place recognition can be broadly split into two categories: computationally expensive deep-learning/image retrieval based techniques with minimal biological plausibility, and computationally cheap, biologically inspired models that yield poor performance in real-world environments. In this paper we present a new compact and high-performing system that bridges this divide for the first time. Our approach comprises two key components: FlyNet, a compact, sparse two-layer neural network inspired by fruit fly brain architectures, and a one-dimensional continuous attractor neural network (CANN). Our FlyNet+CANN network combines the compact pattern recognition capabilities of the FlyNet model with the powerful temporal filtering capabilities of an equally compact CANN, replicating entirely in a neural network implementation the functionality that yields high performance in algorithmic localization approaches like SeqSLAM. We evaluate our approach and compare it to three state-of-the-art methods on two benchmark real-world datasets with small viewpoint changes and extreme appearance variations including different times of day (afternoon to night) where it achieves an AUC performance of 87 and 1
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