Adaptive complementary filtering algorithm for mobile robot localization
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Journal of the Brazilian Computer Society |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002009000300003 |
Resumo: | As a mobile robot navigates through an indoor environment, the condition of the floor is of low (or no) relevance to its decisions. In an outdoor environment, however, terrain characteristics play a major role on the robot's motion. Without an adequate assessment of terrain conditions and irregularities, the robot will be prone to major failures, since the environment conditions may greatly vary. As such, it may assume any orientation about the three axes of its reference frame, which leads to a full six degrees of freedom configuration. The added three degrees of freedom have a major bearing on position and velocity estimation due to higher time complexity of classical techniques such as Kalman filters and particle filters. This article presents an algorithm for localization of mobile robots based on the complementary filtering technique to estimate the localization and orientation, through the fusion of data from IMU, GPS and compass. The main advantages are the low complexity of implementation and the high quality of the results for the case of navigation in outdoor environments (uneven terrain). The results obtained through this system are compared positively with those obtained using more complex and time consuming classic techniques. |
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Journal of the Brazilian Computer Society |
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Adaptive complementary filtering algorithm for mobile robot localizationcomplementary filteringlocalizationoutdoor navigationmobile robotsAs a mobile robot navigates through an indoor environment, the condition of the floor is of low (or no) relevance to its decisions. In an outdoor environment, however, terrain characteristics play a major role on the robot's motion. Without an adequate assessment of terrain conditions and irregularities, the robot will be prone to major failures, since the environment conditions may greatly vary. As such, it may assume any orientation about the three axes of its reference frame, which leads to a full six degrees of freedom configuration. The added three degrees of freedom have a major bearing on position and velocity estimation due to higher time complexity of classical techniques such as Kalman filters and particle filters. This article presents an algorithm for localization of mobile robots based on the complementary filtering technique to estimate the localization and orientation, through the fusion of data from IMU, GPS and compass. The main advantages are the low complexity of implementation and the high quality of the results for the case of navigation in outdoor environments (uneven terrain). The results obtained through this system are compared positively with those obtained using more complex and time consuming classic techniques.Sociedade Brasileira de Computação2009-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002009000300003Journal of the Brazilian Computer Society v.15 n.3 2009reponame:Journal of the Brazilian Computer Societyinstname:Sociedade Brasileira de Computação (SBC)instacron:UFRGS10.1007/BF03194503info:eu-repo/semantics/openAccessAlves Neto,ArmandoMacharet,Douglas GuimarãesCampos,Víctor Costa da SilvaCampos,Mario Fernando Montenegroeng2009-12-17T00:00:00Zoai:scielo:S0104-65002009000300003Revistahttps://journal-bcs.springeropen.com/PUBhttps://old.scielo.br/oai/scielo-oai.phpjbcs@icmc.sc.usp.br1678-48040104-6500opendoar:2009-12-17T00:00Journal of the Brazilian Computer Society - Sociedade Brasileira de Computação (SBC)false |
dc.title.none.fl_str_mv |
Adaptive complementary filtering algorithm for mobile robot localization |
title |
Adaptive complementary filtering algorithm for mobile robot localization |
spellingShingle |
Adaptive complementary filtering algorithm for mobile robot localization Alves Neto,Armando complementary filtering localization outdoor navigation mobile robots |
title_short |
Adaptive complementary filtering algorithm for mobile robot localization |
title_full |
Adaptive complementary filtering algorithm for mobile robot localization |
title_fullStr |
Adaptive complementary filtering algorithm for mobile robot localization |
title_full_unstemmed |
Adaptive complementary filtering algorithm for mobile robot localization |
title_sort |
Adaptive complementary filtering algorithm for mobile robot localization |
author |
Alves Neto,Armando |
author_facet |
Alves Neto,Armando Macharet,Douglas Guimarães Campos,Víctor Costa da Silva Campos,Mario Fernando Montenegro |
author_role |
author |
author2 |
Macharet,Douglas Guimarães Campos,Víctor Costa da Silva Campos,Mario Fernando Montenegro |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Alves Neto,Armando Macharet,Douglas Guimarães Campos,Víctor Costa da Silva Campos,Mario Fernando Montenegro |
dc.subject.por.fl_str_mv |
complementary filtering localization outdoor navigation mobile robots |
topic |
complementary filtering localization outdoor navigation mobile robots |
description |
As a mobile robot navigates through an indoor environment, the condition of the floor is of low (or no) relevance to its decisions. In an outdoor environment, however, terrain characteristics play a major role on the robot's motion. Without an adequate assessment of terrain conditions and irregularities, the robot will be prone to major failures, since the environment conditions may greatly vary. As such, it may assume any orientation about the three axes of its reference frame, which leads to a full six degrees of freedom configuration. The added three degrees of freedom have a major bearing on position and velocity estimation due to higher time complexity of classical techniques such as Kalman filters and particle filters. This article presents an algorithm for localization of mobile robots based on the complementary filtering technique to estimate the localization and orientation, through the fusion of data from IMU, GPS and compass. The main advantages are the low complexity of implementation and the high quality of the results for the case of navigation in outdoor environments (uneven terrain). The results obtained through this system are compared positively with those obtained using more complex and time consuming classic techniques. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002009000300003 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002009000300003 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1007/BF03194503 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Computação |
publisher.none.fl_str_mv |
Sociedade Brasileira de Computação |
dc.source.none.fl_str_mv |
Journal of the Brazilian Computer Society v.15 n.3 2009 reponame:Journal of the Brazilian Computer Society instname:Sociedade Brasileira de Computação (SBC) instacron:UFRGS |
instname_str |
Sociedade Brasileira de Computação (SBC) |
instacron_str |
UFRGS |
institution |
UFRGS |
reponame_str |
Journal of the Brazilian Computer Society |
collection |
Journal of the Brazilian Computer Society |
repository.name.fl_str_mv |
Journal of the Brazilian Computer Society - Sociedade Brasileira de Computação (SBC) |
repository.mail.fl_str_mv |
jbcs@icmc.sc.usp.br |
_version_ |
1754734669999374336 |