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.

翻译：在机器人学领域，脉冲神经网络因其在神经形态硬件上实现时尚未充分开发的潜在能效优势和低延迟特性而日益受到关注。本文重点介绍了脉冲神经网络在视觉位置识别中的三项进展。首先，我们提出了模块化脉冲神经网络，其中每个脉冲神经网络代表一组地理上不相交且不重叠的位置，从而能够为大型环境构建可扩展的网络。其次，我们提出了模块化脉冲神经网络的集成方法，其中多个网络表示同一位置，与单网络模型相比显著提高了准确性。我们的脉冲神经网络结构紧凑，仅包含1500个神经元和474k个突触，这种小规模使其非常适合集成。最后，我们研究了序列匹配在基于脉冲神经网络的视觉位置识别中的作用，该技术利用连续图像来优化位置识别。与其他视觉位置识别技术相比，我们分析了脉冲神经网络对集成和序列匹配的响应特性。我们的贡献凸显了脉冲神经网络在视觉位置识别中的可行性，提供了可扩展且鲁棒的解决方案，为其在各种对能耗敏感的机器人任务中的应用铺平了道路。