Open AccessAn Indoor Mobile Robot Localization in perspective of Analysis and Performance using Unscented Kalman Filter
Author(s) -
Asif Rashid,
Yongping He,
Wenpeng Fu,
Zhiqiang Cao
Publication year - 2022
Publication title -
trends in sciences
Language(s) - English
Resource type - Journals
ISSN - 2774-0226
DOI - 10.48048/tis.2022.3097
Subject(s) - kalman filter , covariance , computer science , extended kalman filter , control theory (sociology) , covariance matrix , position (finance) , diagonal , mobile robot , invariant extended kalman filter , robot , component (thermodynamics) , perspective (graphical) , artificial intelligence , algorithm , mathematics , statistics , control (management) , physics , geometry , finance , economics , thermodynamics
This paper describes a method in an indoor environment for the estimation and position, using an Unscented Kalman Filter (UKF). The UKF algorithm applied for the position estimation proposing a new measurement uncertainty model that fixes the error covariance according to the distance measurement. In addition, this approach sets the non-diagonal component of the error covariance matrix for the uncertainty of the speed information and the measurement uncertainty to a value other than zero. This method is evaluated through an experiment using a wheel-type mobile robot with an LRF sensor in an indoor environment. In this experiment, we differentiate the estimation execution of the proposed approach with a conventional method that does not employ an adaptive uncertainty model. Moreover, the results improved the estimation performance by setting the non-diagonal component of the error covariance to a value other than zero. The main emphasis of this paper is to implement a practical UKF method for location estimation of a mobile robot and analyze it with better performance.