  Spiking neural networks (SNNs) offer both compelling potential advantages,
including energy efficiency and low latencies and challenges including the
non-differentiable nature of event spikes. Much of the initial research in this
area has converted deep neural networks to equivalent SNNs, but this conversion
approach potentially negates some of the advantages of SNN-based approaches
developed from scratch. One promising area for high-performance SNNs is
template matching and image recognition. This research introduces the first
high-performance SNN for the Visual Place Recognition (VPR) task: given a query
image, the SNN has to find the closest match out of a list of reference images.
At the core of this new system is a novel assignment scheme that implements a
form of ambiguity-informed salience, by up-weighting single-place-encoding
neurons and down-weighting "ambiguous" neurons that respond to multiple
different reference places. In a range of experiments on the challenging
Nordland, Oxford RobotCar, SPEDTest, Synthia, and St Lucia datasets, we show
that our SNN achieves comparable VPR performance to state-of-the-art and
classical techniques, and degrades gracefully in performance with an increasing
number of reference places. Our results provide a significant milestone towards
SNNs that can provide robust, energy-efficient, and low latency robot
localization.
