Abstract
Energy-efficient simultaneous localization and mapping (SLAM) is crucial for mobile robots exploring unknown environments. The mammalian brain solves SLAM via a network of specialized neurons, exhibiting asynchronous computations and event-based communications, with very low energy consumption. We propose a brain-inspired spiking neural network (SNN) architecture that solves the unidimensional SLAM by introducing spike-based reference frame transformation, visual likelihood computation, and Bayesian inference. Our proposed SNN is seamlessly integrated into Intel's Loihi neuromorphic processor, a non-Von Neumann hardware that mimics the brain's computing paradigms. We performed comparative analyses for accuracy and energy-efficiency between our method and the GMapping algorithm, which is widely used in small environments. Our Loihi-based SNN architecture consumes 100 times less energy than GMapping run on a CPU while having comparable accuracy in head direction localization and map-generation. These results pave the way for extending our approach towards an energy-efficient SLAM that is applicable to Loihi-controlled mobile robots.
Abstract (translated)
高效节能的同时定位和绘图(SLAM)对于探索未知环境的移动机器人至关重要。哺乳动物的大脑通过专门的神经元网络来解决SLAM问题,表现出异步计算和基于事件的通信,能耗非常低。我们提出了一种基于脑激励的spiking神经网络(snn)结构,该结构通过引入基于spike的参考帧转换、视觉似然计算和贝叶斯推理来解决一维冲击。我们提出的SNN无缝集成到Intel的Loihi神经形态处理器中,这是一个模仿大脑计算模式的非von Neumann硬件。我们对我们的方法和GMAPping算法进行了精度和能量效率的比较分析,GMAPping算法在小型环境中得到了广泛的应用。我们基于LoiHi的SNN架构消耗的能量比在CPU上运行的GMAPping少100倍,同时在头部方向定位和地图生成方面具有相当的准确性。这些结果为将我们的方法扩展到一种适用于Loihi控制的移动机器人的节能冲击上铺平了道路。
URL
https://arxiv.org/abs/1903.02504