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.

在机器人领域，脉冲神经网络（SNNs）在神经元形态硬件上的实现为其高效能源、低延迟带来了巨大的潜力。我们的论文着重介绍了 SNN 在视觉场所识别（VPR）中的三个创新。首先，我们提出模块化 SNN，每个 SNN 代表一组非重叠的地理位置，为大型环境提供可扩展的网络。其次，我们提出模块化 SNN 集合，多个网络代表同一地点，与单一网络模型相比显著提高了准确性。我们的 SNN 非常紧凑且小巧，仅含有 1500 个神经元和 474k 个突触，因此非常适合进行集合操作。最后，我们研究了序列匹配在基于 SNN 的 VPR 中的作用，这是一种使用连续图像进行场所识别的技术。我们分析了 SNN 对集合操作和序列匹配的响应性，并与其他 VPR 技术进行了比较。我们的贡献突出了 SNN 在 VPR 中的可行性，提供了可扩展和稳健的解决方案，为其在各种对能源敏感的机器人任务中的应用铺平了道路。