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@InProceedings{FornariSantShig:2018:SeApAu,
               author = "Fornari, Gabriel and Santiago J{\'u}nior, Valdivino Alexandre de 
                         and Shiguemori, Elcio",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "A self-adaptive approach for autonomous UAV navigation via 
                         computer vision",
                 year = "2018",
         organization = "International Conference on Computational Science and its 
                         Applications, 18. (ICCSA)",
             keywords = "Unmanned Aerial Vehicles, Computer vision Autonomous navigation, 
                         Self-adaptive.",
             abstract = "In autonomous Unmanned Aerial Vehicles (UAVs), the vehicle should 
                         be able to manage itself without the control of a human. In these 
                         cases, it is crucial to have a safe and accurate method for 
                         estimating the position of the vehicle. Although GPS is commonly 
                         employed in this task, it is susceptible to failures by different 
                         means, such as when a GPS signal is blocked by the environment or 
                         by malicious attacks. Aiming to fill this gap, new alternative 
                         methodologies are arising such as the ones based on computer 
                         vision. This work aims to contribute to the process of autonomous 
                         navigation of UAVs using computer vision. Thus, it is presented a 
                         self-adaptive approach for position estimation able to change its 
                         own configuration for increasing its performance. Results show 
                         that an Artificial Neural Network (ANN) presented the best 
                         performance as an edge detector for pictures with buildings or 
                         roads and the Canny extractor was better at smooth surfaces. 
                         Moreover, our selfadaptive approach as a whole shows gain up to 
                         15% if compared with non-adaptive methodologies.",
  conference-location = "Melbourne, Australia",
      conference-year = "02-05 July",
             language = "en",
        urlaccessdate = "26 nov. 2020"
}


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