Hybrid Extended Kalman Filter-based localization with a highly accurate odometry model of a mobile robot

Cong, Tran Huu; Kim, Young Joong; Lim, Myo–Taeg

doi:10.1109/iccas.2008.4694596

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…The position of the robot can not be determined exactly, instead the state estimate with mean ˆk x and covariance matrix k P is produced by Bayesian filter [12][13][14]. There is a number of Bayesian filters suitable for the problem in hand, however when considering computational cost, the simplest one -Extended Kalman Filter (EKF)gives the best performance/cost ration.…”

Section:  mentioning

confidence: 99%

“…Such set of Boolean values is used to calculate the measured angle rel  in a following way. The angles between robot heading direction and position of the i-th receiver on scanner circle ri  are transformed to Cartesian coordinates, the mean is calculated individually for each axis (12) and then resulting angle is recalculated back (13).…”

Section: Infrared Beaconsmentioning

confidence: 99%

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Infrared Beacons based Localization of Mobile Robot

Krejsa¹,

Vĕchet²

2012

ElAEE

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The paper introduces the method for indoor localization of a mobile robot based on infrared beacons positioned stationary in the environment. The beacons provide bearing only information, fused together with either motion controllers commands or robot odometry via Extended Kalman filter to estimate the location of the robot. Simulation results include comparison with UKF and PF based localization and comparison with distance, distance/bearing beacons measurement. The verification on the test real robot with differential chassis and commercial robot with Ackerman chassis is provided. The main advantage of the method is the low cost of whole localization system. Ill. 8, bibl. 17, tabl. 2 (in English; abstracts in English and Lithuanian).

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Section:  mentioning

confidence: 99%

Section: Infrared Beaconsmentioning

confidence: 99%

Infrared Beacons based Localization of Mobile Robot

Krejsa¹,

Vĕchet²

2012

ElAEE

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“…To this purpose, data provided from odometric, laser range finder and MAP are combined together through EKF. The localization based on EKF proposed in the literatures [1][2][3][4][5][6][7][8][9] for the estimation of robot pose. However, a significant difficulty in designing an EKF can often be traced to incomplete a priori knowledge of the process covariance matrix k Q and measurement noise covariance matrix k R [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Extended Kaiman Filtering for robot localization

Havangi

Nekoui

Teshnehlab

2010

Proceedings of 14th International Power Electronics and Motion Control Conference EPE-PEMC 2010

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Extended Kalman Filter (EKF) has been a popular approach to localization a mobile robot. However, the performance of the EKF and the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices ( k Q and k R , respectively). Imprecise knowledge of these statistics can cause significant degradation in performance. This paper proposed the development of an Adaptive Neuro-Fuzzy Extended Kalman Filtering (ANFEKF) for localization of robot. The Adaptive Neuro-Fuzzy attempts to estimate the elements of k Q and k R matrices of the EKF algorithm, at each sampling instant when measurement update step is carried out. The ANFIS supervises the performance of the EKF with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (SD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as much possible. The simulation results show the effectiveness of the proposed algorithm.

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“…Si el robot dispone previamente de un mapa del entorno, el mismo tipo de sensor puede utilizarse para determinar la postura del robot en el mapa, al comparar las características observadas en el entorno con la información almacenada en memoria. De esta forma el robot se puede autolocalizar en el mapa ( [86], [95], [40], [129]). Estas estrategias se caracterizan por un consumo elevado de recursos debido a la complejidad computacional de la solución y en algunos casos debido también al procesamiento requerido por el sensor del entorno, principalmente cuando se utilizan cámaras y hay que aplicar métodos de visión por ordenador para extraer las características del entorno.…”

Section: Localización Y Navegación De Robots Móvilesunclassified

“…En estos casos, la práctica común, según los ejemplos que se expondrán en la sección de antecedentes, es la implementación de los métodos en un ordenador externo ya sea mediante simulación únicamente ( [118], [40], [34], [145], etc.) o bien como un centro de control externo al robot ( [15], [76], [37], [92], [170], etc.)…”

Section: Justificaciónunclassified

Localización de robots móviles de recursos limitados basada en fusión sensorial por eventos

Paniagua¹,

José²

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Palabras clave: Fusión de datos, filtros de Kalman, filtros de Kalman distribuidos, odometría, localización de robots, robots móviles, sistemas basados en eventos, estimación basada en eventos, comunicación basada en eventos, localización cooperativa, sistemas sensoriales, fusión sensorial multirobot, LEGO NXT.Subvenciones: Este trabajo ha sido financiado parcialmente por el Ministerio de Ciencia e Innovación de España bajo los proyectos FEDER/CICYT de investigación DPI2008-06737-C02-01 y DPI2010-20814-C02-02. Además se agradece el soporte financiero por parte de la Universidad de Costa Rica.iv v Aquest mètode a més a més permet fer una fusió sensorial intel·ligent, tenint en compte únicament la informació relativa més fiable, descartant mesures poc precises o procedents de robots molt allunyats.Els algorismes proposats han estat provats extensivament mitjançant simulació i a distintes plataformes mòbils a les quals s'observa el bon acompliment d'objectius per part d'aquests. Es presenten a més a més execucions de llarga durada que comproven l'estabilitat i robustesa del mètode en llargues distàncies. Addicionalment s'analitza, al cas del mètode de localització cooperativa, la relació de compromís entre la covariància de l'error d'estimació i l'ús d'ample de banda en emprar l'algorisme proposat.S'exposa finalment les possibilitats d'ampliació del present treball a àrees com mapeig i localització simultània, ajust del mètode a plataformes omnidireccionals, implementació a distints grups heterogenis de robots i l'estudi de distintes definicions alternatives d'esdeveniments i el seu efecte a l'acompliment de la localització.Paraules clau: Fusió de dades, filtres de Kalman, filtres de Kalman distribuïts, odometria, localització de robots, robots mòbils, sistemes basats en esdeveniments, estimació basada en esdeveniments, comunicació basada en esdeveniments, localització cooperativa, sistemes sensorials, fusió sensorial multirobot, LEGO NXT. Subvencions: Subvencions: Aquest treball ha estat finançat parcialment pel Ministerio de Ciencia e Innovación de España baix els projectes FE-DER/CICYT d'investigació ABSTRACT Localization, the process of obtaining and processing the information of the robot pose in the movement space, is a fundamental issue in autonomous mobile robots as this information is required to determine the desired robot movements. For this task, all the available sensor sources must be used in order to reduce the error in the pose estimation, as the measurements are subject to noise and nonlinearities.The formulation of a new sensor fusion framework that improves the localization of mobile robots with limited computational resources, navigating individually in the environment or in assembled heterogeneous groups, is the central and fundamental issue of this thesis.The proposed algorithms use the dynamic model of an Ackermann and a differential steering mobile robots, along with the Kalman Filter fusion scheme to perform the local pose estimation using the inertial sensors measurements, which is updated ...

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Hybrid Extended Kalman Filter-based localization with a highly accurate odometry model of a mobile robot

Cited by 10 publications

References 10 publications

Infrared Beacons based Localization of Mobile Robot

Infrared Beacons based Localization of Mobile Robot

Adaptive Neuro-Fuzzy Extended Kaiman Filtering for robot localization

Localización de robots móviles de recursos limitados basada en fusión sensorial por eventos

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