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Título
Conditional Weighted Linear Fitting for 2D-LiDAR-Mapping of Indoor SLAM
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
IEEE Transactions on Industrial Informatics
Cita Bibliográfica
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
Editorial
IEEE
Fecha
2024
ISSN
1551-3203
Resumen
[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
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Peer review
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URI
DOI
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