Spiking neural networks have significant potential utility in robotics due to their high energy efficiency on specialized hardware, but proof-of-concept implementations have not yet typically achieved competitive performance or capability with conventional approaches. In this paper, we tackle one of the key practical challenges of scalability by introducing a novel modular ensemble network approach, where compact, localized spiking networks each learn and are solely responsible for recognizing places in a local region of the environment only. This modular approach creates a highly scalable system. However, it comes with a high-performance cost where a lack of global regularization at deployment time leads to hyperactive neurons that erroneously respond to places outside their learned region. Our second contribution introduces a regularization approach that detects and removes these problematic hyperactive neurons during the initial environmental learning phase. We evaluate this new scalable modular system on benchmark localization datasets Nordland and Oxford RobotCar, with comparisons to standard techniques NetVLAD, DenseVLAD, and SAD, and a previous spiking neural network system. Our system substantially outperforms the previous SNN system on its small dataset, but also maintains performance on 27 times larger benchmark datasets where the operation of the previous system is computationally infeasible, and performs competitively with the conventional localization systems.

翻译：脉冲神经网络因其在专用硬件上的高能效，在机器人领域具有显著的应用潜力，但现有概念验证实现通常尚未达到与传统方法相当的竞争性能或能力。本文通过引入一种新颖的模块化集成网络方法，解决了可扩展性这一关键实际挑战：每个紧凑型局部脉冲网络仅学习并负责识别环境局部区域内的地点。这种模块化方法构建了高度可扩展的系统，但伴随高性能代价——部署时因缺乏全局正则化，会导致神经元超活跃，错误响应其学习区域之外的地点。我们的第二项贡献提出一种正则化方法，在初始环境学习阶段检测并移除这些有问题的超活跃神经元。我们在基准定位数据集Nordland和Oxford RobotCar上评估了这种新型可扩展模块化系统，并与标准技术NetVLAD、DenseVLAD、SAD及先前的脉冲神经网络系统进行对比。我们的系统在小型数据集上显著优于先前SNN系统，同时能在27倍于前者规模的基准数据集上保持性能（先前系统在此类数据集上因计算不可行而无法运行），并与传统定位系统表现相当。