Abstract
The probability-based filtering method has been extensively used for solving the simultaneous localization and mapping (SLAM) problem. Generally, the standard filter utilizes the system model and prior stochastic information to approximate the posterior state. However, in the real-time situation, the noise statistics properties are relatively unknown, and the system is inaccurately modeled. Thus the filter divergence might occur in the integration system. Moreover, the expected accuracy might be challenging to be reached due to the absence of the responsive time-varying of both the process and measurement noise statistic which naturally can enlarge the uncertainty in the continuous system. Consequently, the traditional strategy needs to be improved aiming to provide an ability to estimate those properties. In order to accomplish this issue, the new adaptive filter is proposed in this paper, termed as an adaptive smooth variable structure filter (ASVSF). Sequentially, the improved SVSF is derived and implemented; the process and measurement noise statistics are estimated by utilizing the maximum a posteriori (MAP) creation and the weighted exponent concept, and the covariance correction step is added based on the divergence suppression concept. In this paper the ASVSF is applied to overcome the SLAM problem of an autonomous mobile robot; henceforth it is abbreviated as an ASVSF-SLAM algorithm. It is simulated and compared to the classical algorithm. The simulation results demonstrated that the proposed algorithm has better performance, stability, and effectiveness.
Highlights
The position tracking is an unavoidable part of localization that needs to be noticed
This task is well-known as simultaneous localization and mapping (SLAM) [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15] which was first proposed by Smith, Self, and Cheeseman in 1988
By simulating and comparing to the conventional of SVSFSLAM algorithm, it can be noted that the proposed method has better stability and accuracy refers to the benchmark in terms of Root Mean Square Error (RMSE) of estimated path and map
Summary
The position tracking is an unavoidable part of localization that needs to be noticed. The SLAM-based mobile robot navigation has intensively received attention because of some challenging factors that need to be solved such as wide uncertainty, system complexity, inaccurate system model, limited prior information, noise statistics of the process and measurement, computational cost, and filter divergence Those reasons lead to the probability-based estimation [5, 8,9,10,11,12, 16, 17] that has been proposed in many cases of the robot navigation. By simulating and comparing to the conventional of SVSFSLAM algorithm, it can be noted that the proposed method has better stability and accuracy refers to the benchmark in terms of Root Mean Square Error (RMSE) of estimated path and map In this case, RMSE is used as the best measure to know the residual or deviation value which represents the difference between the actual and estimated values [28].
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