@InProceedings{PenhaNetoCampShig:2020:UAAuNa,
author = "Penha Neto, Gerson da and Campos Velho, Haroldo Fraga de and
Shiguemori, Elcio Hideiti",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto de Estudos
Avan{\c{c}}ados (IEAv)}",
title = "UAV autonomous navigation by image processing with uncertainty
trajectory estimation",
booktitle = "Proceedings...",
year = "2020",
editor = "Cursi, J. E. S.",
pages = "211--221",
organization = "International Symposium on Uncertainty Quantification and
Stochastic Modelling, 5.",
publisher = "Springer",
note = "{Lecture Notes in Mechanical Engineering}",
keywords = "Unmanned Aerial Vehicles, Autonomous navigation, Image processing,
Self-configuring neural network, Uncertainty quantification.",
abstract = "Unmanned Aerial Vehicles (UAV) is a technology under strong
development, with application on several fields. For the UAV
autonomous navigation, a standard scheme is to use signal from a
Global Navigation System by Satellite (GNSS) onboard. However,
such signal can suffer natural or human interference. Our approach
applies image processing procedure for the UAV positioning: image
edge extraction and correlation between drone image and
georeferenced satellite image. A data fusion is also applied, for
combining the inertial sensor data and positioning by image. The
data fusion is performed by using neural network. The output from
the data fusion neural network is the correction for the UAV
trajectory. Here, the variance of the trajectory error is also
predicted to quantify the uncertainty.",
conference-location = "Rouen, France",
conference-year = "29 jun. - 03 jul.",
isbn = "978-303053668-8",
issn = "21954356",
language = "en",
targetfile = "penha neto_uav.pdf",
urlaccessdate = "11 maio 2024"
}