Journal
JOURNAL OF NAVIGATION
Volume 71, Issue 2, Pages 389-402Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0373463317000716
Keywords
Mobile robot; Position determination; Data integration; Nonlinear filtering; Cubature Kalman Filter
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The objective of this paper is to accurately determine mobile robots' position and orientation by integrating information received from odometry and an inertial sensor. The position and orientation provided by odometry are subject to different types of errors. To improve the odometry, an inertial measurement unit is exploited to give more reliable attitude information. However, the nonlinear dynamic of these systems and their complexities such as different sources of errors make navigation difficult. Since the dynamic models of navigation systems are nonlinear in practice, in this study, a Cubature Kalman Filter (CKF) has been proposed to estimate and correct the errors of these systems. The information from odometry and a gyroscope are integrated using a CKF. Simulation results are provided to illustrate the superiority and the higher reliability of the proposed approach in comparison with conventional nonlinear filtering algorithms such as an Extended Kalman Filter (EKF).
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