A FastSLAM Based on the Smooth Variable Structure Filter for UAVs

Liu, Yang; Wang, Congqing

doi:10.1109/urai.2018.8441876

Cited by 5 publications

(8 citation statements)

References 21 publications

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“…The SVSF is a relative new predictor-estimator, which is first invented in 2007 [15,16]. Firstly, it was derived from a variable structure filter (VSF) [34] and extended variable structure filter (EVSF) [13,17,35]. Then proceed with a presence of new form by completing it with the error covariance matrix without affecting its accuracy and stability [35,36].…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimation-assisted ASVSF through state covariance-based 2D SLAM algorithm

et al. 2021

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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. Moreover, 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 structure filter (ASVSF) by conditioning the probability density function of a measurement to the unknown parameters at one iteration. This proposed method is assumed to accomplish the localization and direct point-based observation task of a wheeled mobile robot, TurtleBot2. Finally, by realistically simulating it and comparing to a conventional 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). This is an open access article under the CC BY-SA license.

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Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimation-assisted ASVSF through state covariance-based 2D SLAM algorithm

et al. 2021

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“…In most cases of mobile robot navigation, a robot should have the ability to locate its position and gather certain information related to the features of the environment automatically. 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. 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.…”

Section: Introductionmentioning

confidence: 99%

“…Referring to some pieces of literature, it has been experiencing fast and significant development. The SVSF was first initiated in 2007 [1,21,28] which was a derived form referring to its successor termed a Variable Structure Filter (VSF) [34,36] and Extended Variable Structure Filter (EVSF) [1,2,34]. Then we proceed with a presence of new form that revises the original SVSF by adding the error covariance matrix without affecting its accuracy and stability [1,25,28,32].…”

Section: Introductionmentioning

confidence: 99%

“…The SVSF was first initiated in 2007 [1,21,28] which was a derived form referring to its successor termed a Variable Structure Filter (VSF) [34,36] and Extended Variable Structure Filter (EVSF) [1,2,34]. Then we proceed with a presence of new form that revises the original SVSF by adding the error covariance matrix without affecting its accuracy and stability [1,25,28,32]. As a common form of the filtering technique, it was then enhanced by involving the time-varying boundary layer width to replace its previous characteristic [23,28,32,37], and even now there has been existing new second-order SVSF which satisfies both first and second sliding condition [28,38].…”

Section: Introductionmentioning

confidence: 99%

“…The state vector is composed not only by robot state vector but also by the landmark state represented by = [ , , , ] and the full state vector can be denoted by = [ , , ,1 , , 1 , , . .…”

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confidence: 99%

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An ASVSF-SLAM Algorithm with Time-Varying Noise Statistics Based on MAP Creation and Weighted Exponent

Tian

Suwoyo

Wang

et al. 2019

Mathematical Problems in Engineering

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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.

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