Applications of Spiking Neural Networks in Visual Place Recognition

• URL: http://arxiv.org/abs/2311.13186v2
• Date: Fri, 2 Aug 2024 00:56:39 GMT
• Title: Applications of Spiking Neural Networks in Visual Place Recognition
• Authors: Somayeh Hussaini, Michael Milford, Tobias Fischer,
• Abstract summary: Spiking Neural Networks (SNNs) are increasingly recognized for their potential energy efficiency and low latency. This paper highlights three advancements for SNNs in Visual Place Recognition (VPR) Firstly, we propose Modular SNNs, where each SNN represents a set of non-overlapping geographically distinct places. Secondly, we present Ensembles of Modular SNNs, where multiple networks represent the same place. Lastly, we investigate the role of sequence matching in SNN-based VPR, a technique where consecutive images are used to refine place recognition.
• Score: 19.577433371468533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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). Firstly, 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. Each of our Modular SNN modules is compact, comprising only 1500 neurons and 474k synapses, making them ideally suited for ensembling due to their 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, and paving the way for their application in various energy-sensitive robotic tasks.

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