Técnicas de slam con filtros probabilísticos: caracterización y resultados en robots móviles

Franklin Pineda-Torres

doi:10.61799/2216-0388.408

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Slam techniques with probabilistic filters; characterization and results in mobile robots

Franklin Pineda-Torres

Universidad Autónoma de Colombia

DOI: https://doi.org/10.61799/2216-0388.408

Published

2019-10-07

Issue

Vol. 9 No. 18 (2019)

Section

Articulos

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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[1]

Pineda-Torres, F. 2019. Slam techniques with probabilistic filters; characterization and results in mobile robots. Mundo FESC Journal. 9, 18 (Oct. 2019), 7–15. DOI:https://doi.org/10.61799/2216-0388.408.

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Kalman filter, localization, mobile robot, tsensor, slam

he challenge of locating and mapping SLAM in mobile robots is to discover if it is possible to navigate through an unknown environment and incrementally build a consistent map of it, while the robot at the same time determining its position within the map. The theoretical-conceptual solution has been developed by the SLAM especially with location filters, such as: the Kalman filter, the Information filter, the graphic filter and the particle filter among others. The research carried out by varying established marks in the trajectories and analyzing these filters from the probabilistic point of view, these filters are incorporated in two prototypes of mobile robots, which have ultrasound and laser sensors. The article shows the results of errors in odometry and necessary times that each SLAM filter has on average per iteration for the construction of the two-dimensional map.

R. Siegwart, I. Nourbakhsh, D. Scaramuzza “Introduction to Autonomous Mobile Robots”. France: MIT, Second Edition, 2011.

L. Armesto, “Te´cnicas de control y fusio´n sensorial multifrecuenciales y su aplicacio´n a la robo´tica mo´vil”. Tesis doctoral, Universidad Polite´cnica de Valencia, España, 2005.

S. Kim, y B.K. Kim, “Dynamic Ultrasonic Hybrid Localization System for Indoor Mobile Robots”. IEEE Transactions on Industrial Electronics, vol. 60, pp 4562-4573, 2013. DOI: https://doi.org/10.1109/TIE.2012.2216235

G. N. DeSouza y A. C. Kak. “Vision for Mobile Robot Navigation: A Survey”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 2, 2002. DOI: https://doi.org/10.1109/34.982903

J. M. Armingol-Moreno, Localización Geométrica de Robots Móviles Autónomos. Tesis Doctoral Universidad Carlos III de Madrid, España, 1997.

H. Durrant-Whyte, y T.Bailey. “Simultaneous localization and mapping”.: Robotics Automation Magazine, vol. 13 no.2, pp. 1-9, 2006. DOI: https://doi.org/10.1109/MRA.2006.1638022

S. Thrun, W. Burgard, D. Fox, “Probabilistics Robotics”. Massachusetts: The MIT Press, 2005

H. R. Everettt, “Sensors for Mobile Robots, theory and application”. Massachusetts: A.K. Peters Ltd., 1995.

P. Corke, “Robotics, Vision and Contro”. Unite Stated: Springer Publishing, 1 Ed, 2011. DOI: https://doi.org/10.1007/978-3-642-20144-8_1

D. Pérez, Sensores de distancia por ultrasonido, 2017. [En linea]. Disponible en: http://www.alcabot.com/alcabot/seminario2006/Trabajos/DiegoPerezDeDiego.pdf [Accedido: 13-marzo-2019].

F. E. Pineda F. “Localización Probabilística en Drones, para aprendizajes cooperativos”. Informe de Avance. SUI. Universidad Autónoma de Colombia. Bogotá, Colombia, 2017.

C. Fernández. “Técnicas de Navegación de Robots basadas en medición por láser”. Tesis de Pregrado. Universidad de Salamanca, España, 2007.

J. Berrío J. “Mapeo y Localización Simultánea de un Robot Móvil en Ambientes Estructurados Basado En Integración Sensorial”. Tesis de Maestría. Universidad del Valle, Cali, Colombia, 2012.

S. Thrun, “Robotic mapping: A survey. In Exploring Artificial Intelligence in the New Millenium”. Massachusetts: Morgan Kaufmann Publishers2003.,

C. Stachniss. (2017). “Robot Mapping - WS 2013/14”. Germany: UniFreiburg AIS, 2017.

F. Andrade, y M. Llofriu. “Estudio del estado del arte del SLAM e implementacio´n de una plataforma flexible”. Tesis de pregrado. Universidad de la República. Montevideo Uruguay, 2017.

Corke Peter (2017). “Robotics Toolbox for MATLAB”. European: Press Realease 10.

F. E. Pineda F. “Localización Probabilística en Drones, para aprendizajes cooperativos”. Segundo Informe de Avance. SUI. Universidad Autónoma de Colombia. Bogotá, Colombia, 2017.

Keywords:

Kalman filter, localization, mobile robot, tsensor, slam

Abstract

References