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 both standard techniques NetVLAD 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:Spik 神经网络由于在专门硬件上的能源效率较高,因此在机器人中具有巨大的潜在效用,因为其专门硬件的能源效率很高,但概念验证的实施通常尚未以常规方法实现竞争性的性能或能力。在本文件中,我们通过采用新型模块组合混合式网络方法,应对可缩放性的关键实际挑战之一,即采用新型模块组合式混合式网络方法,每个学习并独自负责识别当地环境区域的位置。这种模块化方法创造了一个高度可缩放的系统。然而,由于在部署时缺乏全球正规化导致超活跃神经元,错误地应对其学习区域以外的地点。我们的第二个贡献引入了一种正规化方法,在最初的环境学习阶段发现并消除这些有问题的超活跃神经元。我们评估了这个新的可缩放式模块系统,将标准技术NetVLAD和SAD以及以前的神经网络系统加以比较。我们的系统大大超越了先前的SNN系统在小型数据集上的超活跃神经元系统,但也保持了常规系统在27个时期的竞争性运行。