EKF-Based Localization of a Wheeled Mobile Robot in Structured Environments

Teslić, Luka; Škrjanc, Igor; Klančar, Gregor

doi:10.1007/s10846-010-9441-8

Cited by 77 publications

(43 citation statements)

References 13 publications

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“…This assumption is sufficient and widely adopted due to the central limit theorem [9][10][11][12]. With the existence of noises, the robot system can be described in state-space representation as follows.…”

Section: System Modelmentioning

confidence: 99%

Stable control of networked robot subject to communication delay, packet loss, and out-of-order delivery

2014

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Abstract. Stabilization control of networked robot system faces uncertain factors caused by the network. Our approach for this problem consists of two steps. First, the Lyapunov stability theory is employed to derive control laws that stabilize the non-networked robot system. Those control laws are then extended to the networked robot system by implementing a predictive filter between the sensor and controller. The filter compensates influences of the network to acquire accurate estimate of the system state and consequently ensures the convergence of the control laws. The optimality of the filter in term of minimizing the mean square error is theoretically proven. Many simulations and experiments have been conducted. The result confirmed the validity of the proposed approach.

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Section: System Modelmentioning

confidence: 99%

Stable control of networked robot subject to communication delay, packet loss, and out-of-order delivery

2014

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“…This method using the data obtained from the mobile sensor like encoder to estimate the change of the position of mobile robot with time. However, in the process of localization of mobile robot, it has a inevitable problem of accumulative error when the mobile robot moves long distances [3][4].To improve the accuracy of odometry using in localization estimation of mobile robot, many research have been undergone in the filed of eliminate the systematic error, robot design constraints and environment influences. A common approach consists of two parts.…”

Section: Introductionmentioning

confidence: 99%

Research on EKF-Based Localization Method of Tracked Mobile Robot

Qu¹,

Zhang²,

Hou³

et al. 2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Abstract. To estimate the position and heading angle of mobile robot precisely, an measurement variable estimation model was proposed to adapt any angle. Fusing the predictive value of odometry and measurement data of multiple sensors by the Extended Kalman Filtering (EKF) for reducing the accumulative error by using only traditional odometry. The proposed models is verified by Matlab simulation and experimental results.

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“…In mobile robotics a very popular sensor for this purpose is the laser range finder (LRF), which has good coverage, dense information, high accuracy and a high sampling rate. It can be used for localization purposes, map building or SLAM, as in [5][6][7][8][9][10]. Using a LRF the robot pose can be estimated by comparing a locally sensed map given by a cloud of reflection points and a known map of the environment.…”

Section: Introductionmentioning

confidence: 99%