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arXiv · 2106.09326

Towards bio-inspired unsupervised representation learning for indoor aerial navigation

Ni Wang·Ozan Catal·Tim Verbelen·Matthias Hartmann·Bart Dhoedt
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Abstract

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.

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Ni Wang, Ozan Catal, Tim Verbelen, Matthias Hartmann, Bart Dhoedt. 2021-06-17. Towards bio-inspired unsupervised representation learning for indoor aerial navigation. https://arxiv.org/abs/2106.09326

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