Error estimation and correction in a spiking neural network for map formation in neuromorphic hardware

Abstract

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.