English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In robotics, Spiking Neural Networks (SNNs) are increasingly recognized fortheir largely-unrealized potential energy efficiency and low latencyparticularly when implemented on neuromorphic hardware. Our paper highlightsthree advancements for SNNs in Visual Place Recognition (VPR). First, wepropose Modular SNNs, where each SNN represents a set of non-overlappinggeographically distinct places, enabling scalable networks for largeenvironments. Secondly, we present Ensembles of Modular SNNs, where multiplenetworks represent the same place, significantly enhancing accuracy compared tosingle-network models. Our SNNs are compact and small, comprising only 1500neurons and 474k synapses, which makes them ideally suited for ensembling dueto this small size. Lastly, we investigate the role of sequence matching inSNN-based VPR, a technique where consecutive images are used to refine placerecognition. We analyze the responsiveness of SNNs to ensembling and sequencematching compared to other VPR techniques. Our contributions highlight theviability of SNNs for VPR, offering scalable and robust solutions, paving theway for their application in various energy-sensitive robotic tasks.

Applications of Spiking Neural Networks in Visual Place Recognition