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1. Identity statement
Reference TypeBook Section
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W/3SCS357
Repositorysid.inpe.br/plutao/2018/12.14.19.51.14   (restricted access)
Last Update2018:12.18.17.00.24 (UTC) simone
Metadata Repositorysid.inpe.br/plutao/2018/12.14.19.51.15
Metadata Last Update2024:01.03.12.46.12 (UTC) simone
DOI10.1007/978-3-319-95165-2_19
ISBN9783319951645
Labellattes: 8086526958304657 1 FornariSantShig:2018:SeApAu
Citation KeyFornariSantShig:2018:SeApAu
TitleA self-adaptive approach for autonomous UAV navigation via computer vision
Year2018
Access Date2024, Apr. 26
Secondary TypePRE LI
Number of Files1
Size473 KiB
2. Context
Author1 Fornari, Gabriel
2 Santiago Júnior, Valdivino Alexandre de
3 Shiguemori, Elcio Hideiti
Resume Identifier1
2 8JMKD3MGP5W/3C9JJB5
Group1 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
2 LABAC-COCTE-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 gabriel.fornari@inpe.br
2 valdivino.santiago@inpe.br
EditorGervasi, O.
Murgante, B.
Misra, S.
Stankova, E.
Torre, C. M.
Rocha, A. M. A. C.
Taniar, D.
Apduhan, B. O.
Tarantino, E.
Ryu, Y.
Book TitleComputational science and its applications
PublisherSpringer International Publishing
CityBerlin
Volume10961
Pages268-280
History (UTC)2018-12-14 19:51:15 :: lattes -> administrator ::
2018-12-18 07:23:18 :: administrator -> lattes :: 2018
2018-12-18 17:00:26 :: lattes -> administrator :: 2018
2019-01-14 17:09:18 :: administrator -> simone :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsUnmanned Aerial Vehicles
Computer vision Autonomous navigation
Self-adaptive
AbstractIn 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.
AreaCOMP
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > A self-adaptive approach...
Arrangement 2urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > A self-adaptive approach...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languageen
Target Filefornari_self.pdf
User Grouplattes
Reader Groupadministrator
simone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ESGTTP
8JMKD3MGPCW/3F2PHGS
Citing Item Listsid.inpe.br/bibdigital/2013/09.22.23.14 2
sid.inpe.br/mtc-m21/2012/07.13.15.01.24 1
URL (untrusted data)http://link.springer.com/10.1007/978-3-319-95165-2_19
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
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7. Description control
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