3D robot mapping: combining active and non active sensors in a probabilistic framework

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/1601
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Title: 3D robot mapping: combining active and non active sensors in a probabilistic framework
Authors: Aznar Gregori, Fidel | Sempere Tortosa, Mireia Luisa | Pujol, Mar | Rizo, Ramón | Molina-Carmona, Rafael
Research Group/s: Informática Industrial e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Bayesian units | Laser/Omnivision fusion | 3D mapping
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Date Created: 2005
Issue Date: 2006
Publisher: Springer
Citation: AZNAR GREGORI, Fidel, et al. “3D robot mapping: combining active and non active sensors in a probabilistic framework”. Lecture notes in computer science. 2006, vol. 4177/2006. ISSN 0302-9743, pp. 11-20
Abstract: Map reconstruction and robot location are two essential problems in the field of robotics and artificial intelligence. A robot could need a model of the environment that can be incomplete and therefore the robot must work considering the uncertainty. Bayesian Units consider the uncertainty and allow the fusion of information from different sensors. In this paper a map reconstruction system in 3D based on Bayesian Units is presented. The reconstruction is carried out integrating the data obtained by a laser and by an omnivision system. In addition, to improve the quality of the reconstruction, the fusion of several Bayesian Units is defined using a competitive fusion operator. Finally, the obtained results as well as the validity of the system are shown.
Sponsor: This work has been financed by the Generalitat Valenciana project GV04B685.
URI: http://hdl.handle.net/10045/1601
ISBN: 978-3-540-45914-9
ISSN: 0302-9743
DOI: 10.1007/11881216_2
Language: eng
Type: info:eu-repo/semantics/article
Rights: The original publication is available at www.springerlink.com
Peer Review: si
Publisher version: http://dx.doi.org/10.1007/11881216_2
Appears in Collections:INV - i3a - Artículos de Revistas
INV - Smart Learning - Artículos de Revistas

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