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 …

Date

2020

Authors

Alexander Jones, Andrew Rush, Cory Merkel, Eric Herrmann, Ajey P Jacob, Clare Thiem, Rashmi Jha

Journal

Neurocomputing

Volume

381

Pages

89-104

Publisher

Elsevier