Aerial navigation in GPS-denied, indoor environments, is still an open challenge. Drones can perceive the environment from a richer set of viewpoints, while having more stringent compute and energy constraints than other autonomous platforms. To tackle that problem, this research displays a biologically inspired deep-learning algorithm for simultaneous localization and mapping (SLAM) and its application in a drone navigation system. We propose an unsupervised representation learning method that yields low-dimensional latent state descriptors, that mitigates the sensitivity to perceptual aliasing, and works on power-efficient, embedded hardware. The designed algorithm is evaluated on a dataset collected in an indoor warehouse environment, and initial results show the feasibility for robust indoor aerial navigation.
@article{arxiv.2106.09326,
title = {Towards bio-inspired unsupervised representation learning for indoor aerial navigation},
author = {Ni Wang and Ozan Catal and Tim Verbelen and Matthias Hartmann and Bart Dhoedt},
journal= {arXiv preprint arXiv:2106.09326},
year = {2021}
}