Spiking Neural Networks (SNNs) are at the forefront of neuromorphic computing thanks to their potential energy-efficiency, low latencies, and capacity for continual learning. While these capabilities are well suited for robotics tasks, SNNs have seen limited adaptation in this field thus far. This work introduces a SNN for Visual Place Recognition (VPR) that is both trainable within minutes and queryable in milliseconds, making it well suited for deployment on compute-constrained robotic systems. Our proposed system, VPRTempo, overcomes slow training and inference times using an abstracted SNN that trades biological realism for efficiency. VPRTempo employs a temporal code that determines the timing of a single spike based on a pixel's intensity, as opposed to prior SNNs relying on rate coding that determined the number of spikes; improving spike efficiency by over 100%. VPRTempo is trained using Spike-Timing Dependent Plasticity and a supervised delta learning rule enforcing that each output spiking neuron responds to just a single place. We evaluate our system on the Nordland and Oxford RobotCar benchmark localization datasets, which include up to 27k places. We found that VPRTempo's accuracy is comparable to prior SNNs and the popular NetVLAD place recognition algorithm, while being several orders of magnitude faster and suitable for real-time deployment -- with inference speeds over 50 Hz on CPU. VPRTempo could be integrated as a loop closure component for online SLAM on resource-constrained systems such as space and underwater robots.

翻译：脉冲神经网络（SNN）因其潜在的能效优势、低延迟以及持续学习能力，处于神经形态计算的前沿。尽管这些能力非常适用于机器人任务，但SNN在该领域的应用至今仍十分有限。本研究提出了一种用于视觉地点识别（VPR）的SNN，该网络可在数分钟内完成训练，并在毫秒级别进行查询，非常适合部署于计算受限的机器人系统。我们提出的系统VPRTempo通过采用一种抽象的SNN（牺牲生物真实性以换取高效性）克服了训练和推理时间长的缺点。VPRTempo采用时间编码，根据像素强度确定单个脉冲的触发时间，而非依赖先前SNN中决定脉冲数量的速率编码；这使脉冲效率提升了超过100%。VPRTempo使用脉冲时序依赖可塑性（STDP）以及一种监督delta学习规则进行训练，强制每个输出脉冲神经元仅响应单一地点。我们在包含多达27000个地点的Nordland和RobotCar基准定位数据集上评估了该系统。结果发现，VPRTempo的准确率与先前SNN以及流行的NetVLAD地点识别算法相当，同时速度快数个数量级，适合实时部署——其在CPU上的推理速度超过50 Hz。VPRTempo可作为回环检测组件，集成至资源受限系统（如太空及水下机器人）的在线同步定位与建图（SLAM）中。