Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/43560
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Title: Learning Probabilistic Features for Robotic Navigation Using Laser Sensors
Authors: Aznar Gregori, Fidel | Pujol, Francisco A. | Pujol, Mar | Rizo, Ramón | Pujol López, María José
Research Group/s: Informática Industrial e Inteligencia Artificial | UniCAD: Grupo de Investigación en CAD/CAM/CAE de la Universidad de Alicante
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Matemática Aplicada
Keywords: SLAM-based | Probabilistic robotic system | Learning | Navigation | Laser sensors
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial | Arquitectura y Tecnología de Computadores | Matemática Aplicada
Issue Date: 21-Nov-2014
Publisher: Public Library of Science (PLoS)
Citation: Aznar F, Pujol FA, Pujol M, Rizo R, Pujol M-J (2014) Learning Probabilistic Features for Robotic Navigation Using Laser Sensors. PLoS ONE 9(11): e112507. doi:10.1371/journal.pone.0112507
Abstract: SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.
Sponsor: This work has been supported by the Spanish Ministerio de Ciencia e Innovación (www.micinn.es), project TIN2009-10581.
URI: http://hdl.handle.net/10045/43560
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0112507
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2014 Aznar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Peer Review: si
Publisher version: http://dx.doi.org/10.1371/journal.pone.0112507
Appears in Collections:INV - UNICAD - Artículos de Revistas
INV - i3a - Artículos de Revistas

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