LAUSR

Abstract Navigation in GPS-denied environments is a critical challenge for autonomous mobile platforms such as drones. The concept of simultaneous localization and mapping (SLAM) addresses this challenge through real-time mapping of the platform's surroundings as it explores its environment. The computational resources required for traditional SLAM implementations (e.g. graphical processing units) require large size, weight, and power overheads; making it infeasible to employ them in resource-constrained applications. This work proposes a self-learning hardware architecture utilizing a novel gated-memristive device to address the implementation of SLAM in an energy-efficient manner. The gated-memristive devices are implemented as electronic synapses in tandem with novel low-energy spiking neurons to create a spiking neural network (SNN). This work shows how the SNN allows for navigation through an environment via landmark association without needing GPS. In the simple environment in which the network exists, it can successfully determine a direction in which to navigate while only consuming 36 µW of power and only needing to be exposed to each landmark within the environment for 1-2ms in order to remember that location.