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Multisensor fusion of environment measures using Bayesian Networks

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dc.book.titleFusion'98: Proceedings of the International Conference on Multisource-Multisensor Information Fusion
dc.contributor.authorLópez Orozco, José Antonio
dc.contributor.authorCruz García, Jesús Manuel de la
dc.contributor.authorSanz, J.
dc.contributor.authorFlores, J.
dc.date.accessioned2023-06-20T21:09:41Z
dc.date.available2023-06-20T21:09:41Z
dc.date.issued1998
dc.descriptionInternational Conference on Multisource-Multisensor Information Fusion (FUSION 98) (Jul 06-09, 1998. Las Vegas)
dc.description.abstractAutonomous mobile robots usually require a large number of sensor types and sensing modules. There are different sensors, some complementary and some redundant. Integrating the sensor measures implies several multisensor fusion techniques. These techniques can be classified in two groups: low level fusion, used for direct integration of sensory data; and high level fusion, which is used for indirect integration of sensory data. We have developed a system to integrate indirect measures of different sensors. This system allows us to use any type of sensor which provides measures of the robot's environment It Is designed as a Belief Bayesian Network. The method needs that the user creates a low level fusion module and an interface between that module and our fusion system.
dc.description.departmentSección Deptal. de Arquitectura de Computadores y Automática (Físicas)
dc.description.facultyFac. de Ciencias Físicas
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/26573
dc.identifier.isbn1-892512-02-5
dc.identifier.officialurlhttp://isif.org/fusion/proceedings/fusion98CD/487.pdf
dc.identifier.relatedurlhttp://isif.org
dc.identifier.urihttps://hdl.handle.net/20.500.14352/60853
dc.language.isoeng
dc.page.final493
dc.page.initial487
dc.publisherCSREA Press
dc.rights.accessRightsopen access
dc.subject.cdu004
dc.subject.keywordIntegration
dc.subject.ucmInformática (Informática)
dc.subject.unesco1203.17 Informática
dc.titleMultisensor fusion of environment measures using Bayesian Networks
dc.typebook part
dcterms.references[1] Llinas, J., and Waltz, E. Multisensor Data Fusion. Artech House, Norwood, Massachuusetts, 1990. [2] Luo, R.C. and Kay, M.G. Data Fusion and Sensor Integration: State-of-the-art 1990s. In Data Fusion in Robotics and Machine Intelligence, by Abidi and Gonzalez. Boston, MA, Academic Press, 1992. [3] Abidi, M.A. and Gonzalez R.C. Data Fusion in Robotics and Machine Intelligence. Boston, MA, Academic Press, 1992. [4] Chang C.C., and Song, K. Ultrasonic Sensor Data Integration and Its Application to Environment Perception. Journal of Robotic Systems. 13 (10) pp. 663-677, 1996. [5] Elfes, A. Occupancy Grids: A Stochastic spatial Representation for Active robot Perception. Proceeding of the Sixth Conference on Uncertainty in Al, July 1990. [6] Rigaud, V., and Mareé L. Absolute Location of underwater Robotic Vehicles by Acoustic Data fusion. Int. Conf. on Robotics and Automation. pp. 1310-1315, 1990. [7] Luo, R.C., and Kay M.G. Multisensor Integration and Fusion in Intelligent Systems. IEEE Transactions on Systems, Man, and Cybemetics. 19 (5), pp. 901-931, 1989. [8] Hall, D. L. Mathematical Techniques in Multisensor Data Fusion. Artech House, London, 1992. [9] Kam M. Zhu X. and Kalata P. Sensor Fusion for Mobile Robot Navigation. Proceedings of the IEEE, 85 (1), pp. 108-119. January, 1997. [10] Kessler et al. Functional Description of the Data Fusion Process. Tech. Rep. Office of Naval Technolo. Naval Air Development Ctr. Warminster, PA, Jan. 1992. [11] Viswanathan R. And Varshney P. K. Distributed Detection With Multiple Sensors: Part I- Fundamentals. Proceedings of the IEEE, 85 (1), pp. 54-63. January, 1997. [12] Pearl, J. Probabilistic Reasoning in Intelligent systems, Morgan Kaufmann, San Mateo, California, 1988. [13] Neapolitan, R. E. Probabilistic Reasoning in Expert Systems. Theory and Algorithms. John Wiley & Sons, inc. 1990.
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relation.isAuthorOfPublication26b95994-f79c-4d7c-8de5-a003d6d2a770
relation.isAuthorOfPublication.latestForDiscovery26b95994-f79c-4d7c-8de5-a003d6d2a770

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