A Localization Method Based on Map-Matching and Particle Swarm Optimization

dc.contributor.author Andry Maykol Pinto en
dc.contributor.author António Paulo Moreira en
dc.contributor.author Paulo José Costa en
dc.date.accessioned 2017-12-22T16:02:38Z
dc.date.available 2017-12-22T16:02:38Z
dc.date.issued 2015 en
dc.description.abstract This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory, a new robotic competition which is started in Lisbon in 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that performs well. The sensor information is continuously updated in time and space according to the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, the Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high, meaning that the map-matching is unreliable and the robot gets lost. The experiments presented in this paper prove the ability and accuracy of the presented technique to locate small mobile robots for this competition. Extensive results show that the proposed method presents an interesting localization capability for robots equipped with a limited amount of sensors, but also less reliable sensors. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/4773
dc.identifier.uri http://dx.doi.org/10.1007/s10846-013-0009-2 en
dc.language eng en
dc.relation 5157 en
dc.relation 5446 en
dc.relation 5153 en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title A Localization Method Based on Map-Matching and Particle Swarm Optimization en
dc.type article en
dc.type Publication en
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