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Object detection for indoor localization system

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dc.contributor.authorBraun, João
dc.contributor.authorMendes, João
dc.contributor.authorPereira, Ana I.
dc.contributor.authorLima, José
dc.contributor.authorCosta, Paulo Gomes da
dc.date.accessioned2023-03-01T11:41:43Z
dc.date.available2023-03-01T11:41:43Z
dc.date.issued2023
dc.description.abstractThe urge for robust and reliable localization systems for autonomous mobile robots (AMR) is increasing since the demand for these automated systems is rising in service, industry, and other areas of the economy. The localization of AMRs is one of the crucial challenges, and several approaches exist to solve this. The most well-known localization systems are based on LiDAR data due to their reliability, accuracy, and robustness. One standard method is to match the reference map information with the actual readings from LiDAR or camera sensors, allowing localization to be performed. However, this approach has difficulties handling anything that does not belong to the original map since it affects the matching algorithm’s performance. Therefore, they should be considered outliers. In this paper, a deep learning-based object detection algorithm is not only used for detection but also to classify them as outliers from the localization’s perspective. This is an innovative approach to improve the localization results in a real mobile platform. Results are encouraging, and the proposed methodology is being tested in a real robot.pt_PT
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). The project that gave rise to these results received the support of a fellowship from ”la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI20/11780028.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.citationBraun, João; Mendes, João; Pereira, Ana I.; Lima, José; Costa, Paulo (2023). Object detection for indoor localization system. In 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022.pt_PT
dc.identifier.urihttp://hdl.handle.net/10198/27358
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectObject detectionpt_PT
dc.subjectConvolutional neural networkspt_PT
dc.subjectDeep learningpt_PT
dc.subjectAutonomous mobile robotpt_PT
dc.subjectIndoor positioningpt_PT
dc.subjectIndoor localizationpt_PT
dc.titleObject detection for indoor localization systempt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/05757/2020
oaire.awardNumberUIDP/05757/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.citation.conferencePlacePóvoa de Varzimpt_PT
oaire.citation.titleInternational Conference on Optimization Learning Algorithms and Applicationspt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameBraun
person.familyNameMendes
person.familyNamePereira
person.familyNameLima
person.givenNameJoão A.
person.givenNameJoão
person.givenNameAna I.
person.givenNameJosé
person.identifier2726655
person.identifierR-000-8GD
person.identifier.ciencia-idBF13-D66B-7D08
person.identifier.ciencia-idEA1F-844D-6BA9
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.ciencia-id6016-C902-86A9
person.identifier.orcid0000-0003-0276-4314
person.identifier.orcid0000-0003-0979-8314
person.identifier.orcid0000-0003-3803-2043
person.identifier.orcid0000-0001-7902-1207
person.identifier.ridF-3168-2010
person.identifier.ridL-3370-2014
person.identifier.scopus-author-id57211244317
person.identifier.scopus-author-id57225794972
person.identifier.scopus-author-id15071961600
person.identifier.scopus-author-id55851941311
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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