%0 Book Section
%3 fornari_self.pdf
%4 sid.inpe.br/plutao/2018/12.14.19.51.14
%A Fornari, Gabriel,
%A Santiago Júnior, Valdivino Alexandre de,
%A Shiguemori, Elcio Hideiti,
%@secondarytype PRE LI
%B Computational science and its applications
%C Berlin
%D 2018
%E Gervasi, O.,
%E Murgante, B.,
%E Misra, S.,
%E Stankova, E.,
%E Torre, C. M.,
%E Rocha, A. M. A. C.,
%E Taniar, D.,
%E Apduhan, B. O.,
%E Tarantino, E.,
%E Ryu, Y.,
%F lattes: 8086526958304657 1 FornariSantShig:2018:SeApAu
%I Springer International Publishing
%K Unmanned Aerial Vehicles, Computer vision Autonomous navigation, Self-adaptive.
%P 268-280
%T A self-adaptive approach for autonomous UAV navigation via computer vision
%U http://link.springer.com/10.1007/978-3-319-95165-2_19
%V 10961
%X 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.
%@area COMP
%@electronicmailaddress gabriel.fornari@inpe.br
%@electronicmailaddress valdivino.santiago@inpe.br
%@documentstage not transferred
%@group CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
%@group LABAC-COCTE-INPE-MCTIC-GOV-BR
%@isbn 9783319951645
%@usergroup lattes
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JJB5
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP
%@nexthigherunit 8JMKD3MGPCW/3F2PHGS
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@versiontype publisher
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@doi 10.1007/978-3-319-95165-2_19
%2 sid.inpe.br/plutao/2018/12.14.19.51.15