Applications of Spiking Neural Networks in Visual Place Recognition
Somayeh Hussaini Michael Milford Tobias Fischer
In robotics, Spiking Neural Networks (SNNs) are increasingly recognized for
their largely-unrealized potential energy efficiency and low latency
particularly when implemented on neuromorphic hardware. Our paper highlights
three advancements for SNNs in Visual Place Recognition (VPR). First, we
propose Modular SNNs, where each SNN represents a set of non-overlapping
geographically distinct places, enabling scalable networks for large
environments. Secondly, we present Ensembles of Modular SNNs, where multiple
networks represent the same place, significantly enhancing accuracy compared to
single-network models. Our SNNs are compact and small, comprising only 1500
neurons and 474k synapses, which makes them ideally suited for ensembling due
to this small size. Lastly, we investigate the role of sequence matching in
SNN-based VPR, a technique where consecutive images are used to refine place
recognition. We analyze the responsiveness of SNNs to ensembling and sequence
matching compared to other VPR techniques. Our contributions highlight the
viability of SNNs for VPR, offering scalable and robust solutions, paving the
way for their application in various energy-sensitive robotic tasks.