@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 = "28 abr. 2024"
}