Evaluations of different Simultaneous Localization and Mapping (SLAM) algorithms

Basit Öğe Kaydı
dc.authorscopusid48862103700
dc.authorscopusid6602534829
dc.authorscopusid10739803300
dc.authorscopusid11540535700
dc.contributor.authorTuna G.
dc.contributor.authorGulez K.
dc.contributor.authorCagri Gungor V.
dc.contributor.authorVeli Mumcu T.
dc.date.accessioned2024-06-12T10:25:23Z
dc.date.available2024-06-12T10:25:23Z
dc.date.issued2012
dc.descriptionThe Institute of Electrical and Electronics Engineers (IEEE);IEEE Industrial Electronics Society (IES)en_US
dc.description38th Annual Conference on IEEE Industrial Electronics Society, IECON 2012 -- 25 October 2012 through 28 October 2012 -- Montreal, QC -- 95131en_US
dc.description.abstractSimultaneous Localization and Mapping (SLAM) algorithms with multiple autonomous robots have received considerable attention in recent years. In general, SLAM algorithms use odometry information and measurements from exteroceptive sensors of robots. The accuracy of these measurements and the performance of the corresponding SLAM algorithm directly affect the overall success of the system. This paper presents comparative performance evaluations of three Simultaneous Localization and Mapping (SLAM) algorithms using Extended Kalman Filter (EKF), Compressed Extended Kalman Filter (CEKF) and Unscented Kalman Filter (UKF). Specifically, it focuses on their SLAM performances and processing time requirements. To show the effect of CPU power on the processing time of SLAM algorithms, two notebooks and a netbook with different specifications have been used. Comparative simulation results show that processing time requirements are consistent with the computational complexities of SLAM algorithms. The results we obtained are consistent with the CPU power tests of independent organizations and show that higher processing power decreases processing time accordingly. The results also show that CEKF is more suitable for outdoor SLAM applications where there are a lot of natural and artificial features. © 2012 IEEE.en_US
dc.identifier.doi10.1109/IECON.2012.6389151
dc.identifier.endpage2698en_US
dc.identifier.isbn9.78147E+12
dc.identifier.scopus2-s2.0-84872938827en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage2693en_US
dc.identifier.urihttps://doi.org/10.1109/IECON.2012.6389151
dc.identifier.urihttps://hdl.handle.net/20.500.14551/16308
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofIECON Proceedings (Industrial Electronics Conference)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComparative Simulation; Compressed Extended Kalman Filters; Cpu Power; Exteroceptive Sensor; Odometry; Performance Evaluation; Processing Power; Processing Time; Simultaneous Localization And Mapping Algorithms; Slam Algorithm; Unscented Kalman Filter; Algorithms; Extended Kalman Filters; Industrial Electronics; Laptop Computers; Roboticsen_US
dc.titleEvaluations of different Simultaneous Localization and Mapping (SLAM) algorithmsen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu