<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>sibgrapi.sid.inpe.br 802</site>
		<identifier>8JMKD3MGPAW/3S3PUPL</identifier>
		<repository>sid.inpe.br/sibgrapi/2018/10.20.00.03</repository>
		<lastupdate>2018:10.20.00.03.10 sid.inpe.br/banon/2001/03.30.15.38 suanepires@lapisco.ifce.edu.br</lastupdate>
		<metadatarepository>sid.inpe.br/sibgrapi/2018/10.20.00.03.10</metadatarepository>
		<metadatalastupdate>2020:02.20.22.06.50 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2018}</metadatalastupdate>
		<citationkey>HonórioFoSiAlMaCaRoRe:2018:ApNaOu</citationkey>
		<title>An Approach to Navigation in Outdoor and Indoor Environments with Unmanned Aerial Vehicle Using Visual Topological Map</title>
		<format>On-line</format>
		<year>2018</year>
		<date>Oct. 29 - Nov. 1, 2018</date>
		<numberoffiles>1</numberoffiles>
		<size>142 KiB</size>
		<author>Honório Filho, Paulo,</author>
		<author>Silva, Suane Pires P. da,</author>
		<author>Almeida, Jefferson S.,</author>
		<author>Marinho, Leandro B.,</author>
		<author>Carneiro, Tiago,</author>
		<author>Rodrigues, Antonio Wendell de O.,</author>
		<author>Rebouças Filho, Pedro Pedrosa,</author>
		<affiliation>Programa de Pós-Graduação em Ciência da Computação (PPGCC), Instituto Federal do Ceará, Fortaleza, Ceará, Brazil</affiliation>
		<affiliation>Programa de Pós-Graduação em Ciência da Computação (PPGCC), Instituto Federal do Ceará, Fortaleza, Ceará, Brazil</affiliation>
		<affiliation>Programa de Pós-Graduação em Ciência da Computação (PPGCC), Instituto Federal do Ceará, Fortaleza, Ceará, Brazil</affiliation>
		<affiliation>Programa de Pós-Graduação em Engenharia de Teleinformática (PPGETI), Universidade Federal do Ceará, Fortaleza, Ceará, Brazil</affiliation>
		<affiliation>Programa de Pós-Graduação em Ciência da Computação (PPGCC), Instituto Federal do Ceará, Fortaleza, Ceará, Brazil</affiliation>
		<affiliation>Programa de Pós-Graduação em Ciência da Computação (PPGCC), Instituto Federal do Ceará, Fortaleza, Ceará, Brazil</affiliation>
		<affiliation>Programa de Pós-Graduação em Ciência da Computação (PPGCC), Instituto Federal do Ceará, Fortaleza, Ceará, Brazil</affiliation>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<e-mailaddress>suanepires@lapisco.ifce.edu.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<documentstage>not transferred</documentstage>
		<transferableflag>1</transferableflag>
		<tertiarytype>Work in Progress</tertiarytype>
		<keywords>Unmanned Aerial Vehicles, Computer Vision, Topological Maps, UAV Navigation.</keywords>
		<abstract>Unmanned Aerial Vehicles (UAVs) are increasingly being applied in professional activities that require higher precision in navigating and positioning the aircraft in flight. Advanced location technologies such as GNSS (Global Navigation Satellite System) and RTK (Real-Time Kinematic), can raise the cost of demand using UAVs or still be dependent on an area with a transmission coverage. In this context, this article presents a visual navigation methodology based on topological maps comparing the performance of consolidated classifiers such as Bayesian classifier, k-Nearest Neighbor (kNN), Multi-layer Perceptron (MLP), Optimum-Path Forest (OPF) and Support Vector Machines (SVM), using attributes returned by state-of-the-art feature extractors such as Fourier, Gray Level Co-Occurrence (GLCM) and Local Binary Patterns (LBP). The results show that the combination of LBP with SVM obtained the best values in the evaluation metrics considered, among them, 99.99% of Specificity and 99.98% of Accuracy in the navigation process.</abstract>
		<language>en</language>
		<targetfile>paulo_sibgrapi.pdf</targetfile>
		<usergroup>suanepires@lapisco.ifce.edu.br</usergroup>
		<visibility>shown</visibility>
		<mirrorrepository>sid.inpe.br/banon/2001/03.30.15.38.24</mirrorrepository>
		<nexthigherunit>8JMKD3MGPAW/3RPADUS</nexthigherunit>
		<hostcollection>sid.inpe.br/banon/2001/03.30.15.38</hostcollection>
		<agreement>agreement.html .htaccess .htaccess2</agreement>
		<lasthostcollection>sid.inpe.br/banon/2001/03.30.15.38</lasthostcollection>
		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2018/10.20.00.03</url>
	</metadata>
</metadatalist>