TELKOMNIKA Telecommunication, Computing, Electronics and Control
Maximum likelihood estimation-assisted ASVSF through state covariance-based 2D SLAM algorithm

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Maximum likelihood estimation-assisted ASVSF through state covariance-based 2D SLAM algorithm

Adaptive smooth variable
structure filter
Maximum likelihood
estimator
Simultaneous localization and
mapping
State estimation
Wheeled mobile robot

The smooth variable structure filter (ASVSF) has been relatively considered as a new
robust predictor-corrector method for estimating the state. In order to effectively utilize
it, an SVSF requires the accurate system model, and exact prior knowledge includes
both the process and measurement noise statistic. Unfortunately, the system model is

always inaccurate because of some considerations avoided at the beginning. More-
over, the small addictive noises are partially known or even unknown. Of course, this

limitation can degrade the performance of SVSF or also lead to divergence condition.

For this reason, it is proposed through this paper an adaptive smooth variable struc-
ture filter (ASVSF) by conditioning the probability density function of a measurement

to the unknown parameters at one iteration. This proposed method is assumed to ac-
complish the localization and direct point-based observation task of a wheeled mobile

robot, TurtleBot2. Finally, by realistically simulating it and comparing to a conven-
tional method, the proposed method has been showing a better accuracy and stability

in term of root mean square error (RMSE) of the estimated map coordinate (EMC) and
estimated path coordinate (EPC).

Heru Suwoyo, Yingzhong Tian, Wenbin Wang, Long Li, Andi Adriansyah, Fengfeng Xi, Guangjie Yuan

http://journal.uad.ac.id/index.php/TELKOMNIKA

Adaptive smooth variable
structure filter
Maximum likelihood
estimator
Simultaneous localization and
mapping
State estimation
Wheeled mobile robot

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