神经形态硬件中地图形成的尖峰神经网络误差估计与校正

Raphaela Kreiser, Gabriel Waibel, Nuria Armengol, Alpha Renner, Yulia Sandamirskaya
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引用次数: 13

摘要

神经形态硬件为峰值神经网络(snn)的有效实现提供了计算平台,可用于机器人控制。在这里,我们在神经形态芯片上提出了这样一个SNN,它解决了许多与同时定位和映射(SLAM)相关的任务:形成未知环境的地图,同时估计机器人的姿势。特别是,我们提出了一种SNN机制来检测和估计机器人重新访问已知地标时的误差,并更新地图和路径集成速度以减少误差。整个系统在神经形态器件中完全实现,表明了纯基于snn的SLAM的可行性,可以在小尺寸神经形态芯片中高效实现。
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Error estimation and correction in a spiking neural network for map formation in neuromorphic hardware
Neuromorphic hardware offers computing platforms for the efficient implementation of spiking neural networks (SNNs) that can be used for robot control. Here, we present such an SNN on a neuromorphic chip that solves a number of tasks related to simultaneous localization and mapping (SLAM): forming a map of an unknown environment and, at the same time, estimating the robot's pose. In particular, we present an SNN mechanism to detect and estimate errors when the robot revisits a known landmark and updates both the map and the path integration speed to reduce the error. The whole system is fully realized in a neuromorphic device, showing the feasibility of a purely SNN-based SLAM, which could be efficiently implemented in a small form-factor neuromorphic chip.
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