000112515 001__ 112515
000112515 005__ 20240319080954.0
000112515 0247_ $$2doi$$a10.3390/s22041390
000112515 0248_ $$2sideral$$a128232
000112515 037__ $$aART-2022-128232
000112515 041__ $$aeng
000112515 100__ $$aSeco, Teresa
000112515 245__ $$aRobot Localization in Tunnels: Combining Discrete Features in a Pose Graph Framework; 35214292
000112515 260__ $$c2022
000112515 5060_ $$aAccess copy available to the general public$$fUnrestricted
000112515 5203_ $$aRobot localization inside tunnels is a challenging task due to the special conditions of these environments. The GPS-denied nature of these scenarios, coupled with the low visibility, slippery and irregular surfaces, and lack of distinguishable visual and structural features, make traditional robotics methods based on cameras, lasers, or wheel encoders unreliable. Fortunately, tunnels provide other types of valuable information that can be used for localization purposes. On the one hand, radio frequency signal propagation in these types of scenarios shows a predictable periodic structure (periodic fadings) under certain settings, and on the other hand, tunnels present structural characteristics (e.g., galleries, emergency shelters) that must comply with safety regulations. The solution presented in this paper consists of detecting both types of features to be introduced as discrete sources of information in an alternative graph-based localization approach. The results obtained from experiments conducted in a real tunnel demonstrate the validity and suitability of the proposed system for inspection applications. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
000112515 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T45-20R$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/PID2019-105390RB-I00
000112515 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000112515 590__ $$a3.9$$b2022
000112515 592__ $$a0.764$$b2022
000112515 591__ $$aCHEMISTRY, ANALYTICAL$$b26 / 86 = 0.302$$c2022$$dQ2$$eT1
000112515 593__ $$aInstrumentation$$c2022$$dQ1
000112515 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b19 / 63 = 0.302$$c2022$$dQ2$$eT1
000112515 593__ $$aAnalytical Chemistry$$c2022$$dQ1
000112515 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b100 / 274 = 0.365$$c2022$$dQ2$$eT2
000112515 593__ $$aMedicine (miscellaneous)$$c2022$$dQ2
000112515 593__ $$aInformation Systems$$c2022$$dQ2
000112515 593__ $$aBiochemistry$$c2022$$dQ2
000112515 593__ $$aAtomic and Molecular Physics, and Optics$$c2022$$dQ2
000112515 593__ $$aElectrical and Electronic Engineering$$c2022$$dQ2
000112515 594__ $$a6.8$$b2022
000112515 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000112515 700__ $$aLázaro, María T.
000112515 700__ $$aEspelosín, Jesús
000112515 700__ $$0(orcid)0000-0002-0449-2300$$aMontano, Luis$$uUniversidad de Zaragoza
000112515 700__ $$0(orcid)0000-0002-7148-4642$$aVillarroel, José L.$$uUniversidad de Zaragoza
000112515 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000112515 773__ $$g22, 4 (2022), 1390 - [35 pp]$$pSensors$$tSensors$$x1424-8220
000112515 8564_ $$s16658092$$uhttps://zaguan.unizar.es/record/112515/files/texto_completo.pdf$$yVersión publicada
000112515 8564_ $$s2686728$$uhttps://zaguan.unizar.es/record/112515/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000112515 909CO $$ooai:zaguan.unizar.es:112515$$particulos$$pdriver
000112515 951__ $$a2024-03-18-13:22:41
000112515 980__ $$aARTICLE