Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition

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.

尖峰神经网络在机器人技术上具有很大的潜在效用，因为它们在专业硬件上的高能量效率很高，但是概念验证实现通常尚未通过常规方法实现竞争性能或能力。在本文中，我们通过引入一种新型的模块化整体网络方法来应对可扩展性的关键实践挑战之一，在这种方法中，紧凑的，局部的尖峰网络每个人都学习，并且仅负责仅在环境的局部区域中识别位置。这种模块化方法创建了一个高度可扩展的系统。但是，它带有高性能成本，在部署时间缺乏全球正规化会导致过度活跃的神经元，这些神经元错误地对其学到的地区以外的地方做出了错误的反应。我们的第二个贡献介绍了一种正则化方法，该方法在初始环境学习阶段检测并消除了这些有问题的多动神经元。我们在基准定位数据集Nordland和Oxford Robotcar上评估了这种新的可扩展模块化系统，并与标准技术Netvlad，Densevlad和SAD进行了比较，以及先前的尖峰神经网络系统。我们的系统在其小数据集上大大胜过先前的SNN系统，但在27倍的基准数据集上保持了性能，在该数据集上，以前系统的操作在计算上是不可行的，并且与传统的本地化系统竞争性能。