  Spiking Neural Networks (SNNs) are at the forefront of neuromorphic computing
thanks to their potential energy-efficiency, low latencies, and capacity for
continual learning. While these capabilities are well suited for robotics
tasks, SNNs have seen limited adaptation in this field thus far. This work
introduces a SNN for Visual Place Recognition (VPR) that is both trainable
within minutes and queryable in milliseconds, making it well suited for
deployment on compute-constrained robotic systems. Our proposed system,
VPRTempo, overcomes slow training and inference times using an abstracted SNN
that trades biological realism for efficiency. VPRTempo employs a temporal code
that determines the timing of a single spike based on a pixel's intensity, as
opposed to prior SNNs relying on rate coding that determined the number of
spikes; improving spike efficiency by over 100%. VPRTempo is trained using
Spike-Timing Dependent Plasticity and a supervised delta learning rule
enforcing that each output spiking neuron responds to just a single place. We
evaluate our system on the Nordland and Oxford RobotCar benchmark localization
datasets, which include up to 27k places. We found that VPRTempo's accuracy is
comparable to prior SNNs and the popular NetVLAD place recognition algorithm,
while being several orders of magnitude faster and suitable for real-time
deployment -- with inference speeds over 50 Hz on CPU. VPRTempo could be
integrated as a loop closure component for online SLAM on resource-constrained
systems such as space and underwater robots.
