000124464 001__ 124464
000124464 005__ 20241125101151.0
000124464 0247_ $$2doi$$a10.1109/LRA.2022.3233230
000124464 0248_ $$2sideral$$a132859
000124464 037__ $$aART-2023-132859
000124464 041__ $$aeng
000124464 100__ $$0(orcid)0000-0002-1361-9529$$aPlaced, Julio A.$$uUniversidad de Zaragoza
000124464 245__ $$aA general relationship between optimality criteria and connectivity indices for active graph-slam
000124464 260__ $$c2023
000124464 5060_ $$aAccess copy available to the general public$$fUnrestricted
000124464 5203_ $$aQuantifying uncertainty is a key stage in active simultaneous localization and mapping (SLAM), as it allows to identify the most informative actions to execute. However, dealing with full covariance or even Fisher information matrices (FIMs) is computationally heavy and easily becomes intractable for online systems. In this letter, we study the paradigm of active graph-SLAM formulated over the special Euclidean group SE(n) , and propose a general relationship between the FIM of the system and the Laplacian matrix of the underlying pose-graph. This link makes possible to use graph connectivity indices as utility functions with optimality guarantees, since they approximate the well-known optimality criteria that stem from optimal design theory. Experimental validation demonstrates that the proposed method leads to equivalent decisions for active SLAM in a fraction of the time.
000124464 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T45-20R$$9info:eu-repo/grantAgreement/ES/MINECO/PID2019-108398GB-I00
000124464 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000124464 590__ $$a4.6$$b2023
000124464 592__ $$a2.119$$b2023
000124464 591__ $$aROBOTICS$$b12 / 46 = 0.261$$c2023$$dQ2$$eT1
000124464 593__ $$aArtificial Intelligence$$c2023$$dQ1
000124464 593__ $$aBiomedical Engineering$$c2023$$dQ1
000124464 593__ $$aComputer Science Applications$$c2023$$dQ1
000124464 593__ $$aMechanical Engineering$$c2023$$dQ1
000124464 593__ $$aControl and Optimization$$c2023$$dQ1
000124464 593__ $$aControl and Systems Engineering$$c2023$$dQ1
000124464 593__ $$aHuman-Computer Interaction$$c2023$$dQ1
000124464 593__ $$aComputer Vision and Pattern Recognition$$c2023$$dQ1
000124464 594__ $$a9.6$$b2023
000124464 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000124464 700__ $$0(orcid)0000-0001-5977-8720$$aCastellanos, José A.$$uUniversidad de Zaragoza
000124464 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000124464 773__ $$g8, 2 (2023), 816-823$$pIEEE Robot. autom. let.$$tIEEE Robotics and Automation Letters$$x2377-3766
000124464 8564_ $$s553380$$uhttps://zaguan.unizar.es/record/124464/files/texto_completo.pdf$$yPostprint
000124464 8564_ $$s3679922$$uhttps://zaguan.unizar.es/record/124464/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000124464 909CO $$ooai:zaguan.unizar.es:124464$$particulos$$pdriver
000124464 951__ $$a2024-11-22-12:07:03
000124464 980__ $$aARTICLE