dc.contributor | Escuela de Ingenierias Industrial, Informática y Aeroespacial | es_ES |
dc.contributor.author | Prieto Fernández, Natalia | |
dc.contributor.author | Fernández Blanco, Sergio | |
dc.contributor.author | Fernández Blanco, Álvaro | |
dc.contributor.author | Benítez Andrades, José Alberto | |
dc.contributor.author | Carro de Lorenzo, Francisco Julián | |
dc.contributor.author | Benavides Cuéllar, María del Carmen | |
dc.contributor.other | Ingenieria de Sistemas y Automatica | es_ES |
dc.date | 2024 | |
dc.date.accessioned | 2024-05-02T08:10:39Z | |
dc.date.available | 2024-05-02T08:10:39Z | |
dc.identifier.citation | Prieto-Fernández, N., Fernández-Blanco, S., Fernández-Blanco, Á., Benítez-Andrades, J. A., Carro-De-Lorenzo, F., and Benavides, C. (2024). Conditional Weighted Linear Fitting for 2D-LiDAR-Mapping of Indoor SLAM. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2024.3384626 | es_ES |
dc.identifier.issn | 1551-3203 | |
dc.identifier.uri | https://hdl.handle.net/10612/20233 | |
dc.description.abstract | [EN] The ability to map an unknown environment is a fundamental milestone for autonomous robotic vehicles. Solutions in this field must combine efficiency, accuracy, and precision. We propose a novel methodology for map feature extraction in indoor environments. The mathematical model and its implementation are designed to operate with 2-D light detection and ranging (LiDAR) measurements. Map parameters and associated uncertainty levels are determined through bivariate linear regression. The final step is experimental validation, using a low-cost commercial LiDAR sensor. The main contributions of the proposed methodology lie in the domains of computational efficiency and uncertainty. In addition, the results prove the ability of our methodology to handle large volumes of data while maintaining restrained growth in computational time. This outcome suggests considerable potential for real-time applications with limited hardware resources. A second methodology, extracted from the current state of the art, is used in parallel for benchmarking purposes | es_ES |
dc.language | eng | es_ES |
dc.publisher | IEEE | es_ES |
dc.rights | Atribución-CompartirIgual 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | * |
dc.subject | Ingenierías | es_ES |
dc.subject | Matemáticas | es_ES |
dc.subject.other | Bivariate linear regression mapping | es_ES |
dc.subject.other | LiDAR | es_ES |
dc.subject.other | SLAM | es_ES |
dc.subject.other | Computational efficiency | es_ES |
dc.subject.other | Feature extraction | es_ES |
dc.subject.other | Light detection and ranging | es_ES |
dc.subject.other | Simultaneous localization and mapping | es_ES |
dc.title | Conditional Weighted Linear Fitting for 2D-LiDAR-Mapping of Indoor SLAM | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.identifier.doi | 10.1109/TII.2024.3384626 | |
dc.description.peerreviewed | SI | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.essn | 1941-0050 | |
dc.journal.title | IEEE Transactions on Industrial Informatics | es_ES |
dc.page.initial | 1 | es_ES |
dc.page.final | 9 | es_ES |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |
dc.subject.unesco | 3317.02 Automóviles | es_ES |
dc.subject.unesco | 3304.13 Dispositivos de Transmisión de Datos | es_ES |
dc.description.project | TRESCA Ingeniería S.A. | es_ES |
dc.description.project | ERDF | es_ES |
dc.description.project | Regional Government of Castilla y León, through the institute for the Business Competitiveness of Castilla y León (ICE) | es_ES |