Marín, LeonardoVallés Miquel, MarinaSoriano Vigueras, ÁngelValera Fernández, ÁngelAlbertos Pérez, Pedro2014-07-082014-081083-4435https://riunet.upv.es/handle/10251/38664“© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”This paper presents a local sensor fusion technique with an event-based global position correction to improve the localization of a mobile robot with limited computational resources. The proposed algorithms use a modified Kalman filter and a new local dynamic model of an Ackermann steering mobile robot. It has a similar performance but faster execution when compared to more complex fusion schemes, allowing its implementation inside the robot. As a global sensor, an event-based position correction is implemented using the Kalman filter error covariance and the position measurement obtained from a zenithal camera. The solution is tested during a long walk with different trajectories using a LEGO Mindstorm NXT robot.12Reserva de todos los derechosDynamic modelKalman filteringEmbedded systemsEvent-based systemsGlobal positioning systems (GPSs)Inertial sensorsMobile robotsPose estimationPosition measurementRobot sensing systemsSensor fusionINGENIERIA DE SISTEMAS Y AUTOMATICAArtículo10.1109/TMECH.2013.2277271Abierto