Bioinspired Direct Visual Estimation of Attitude Rates with Very Low Resolution Images using Deep Networks
M. Mérida-Floriano, F. Caballero, D. Acedo, D. García-Morales, F. Casares, L. Merino
Abstract
In this work we present a bioinspired visual system sensor to estimate angular rates in unmanned aerial vehicles (UAV) using Neural Networks. We have conceived a hardware setup to emulate Drosophila's ocellar system, three simple eyes related to stabilization. This device is composed of three low resolution cameras with a similar spatial configuration as the ocelli. There have been previous approaches based on this ocellar system, most of them considering assumptions such as known light source direction or a punctual light source. In contrast, here we present a learning approach using Artificial Neural Networks in order to recover the system's angular rates indoors and outdoors without previous knowledge. A classical computer vision based method is also derived to be used as a benchmark for the learning approach. The method is validated with a large dataset of images (more than half a million samples) including synthetic and real data. The source code of the algorithms and the datasets used in this paper have been released in an open repository.
BibTeX
@inproceedings{icra2019_bioinspireddirec,
title = {Bioinspired Direct Visual Estimation of Attitude Rates with Very Low Resolution Images using Deep Networks},
author = {M. Mérida-Floriano and F. Caballero and D. Acedo and D. García-Morales and F. Casares and L. Merino},
booktitle = {ICRA 2019},
year = {2019}
}