An AEKF-SLAM Algorithm with Recursive Noise Statistic Based on MLE and EM

Tian, Yingzhong; Suwoyo, Heru; Wang, Wenbin; Mbemba, Dziki; Li, Long

doi:10.1007/s10846-019-01044-8

Cited by 15 publications

(23 citation statements)

References 18 publications

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“…The proposed method is approached to address a feature-based SLAM problem of the wheeled mobile robot [14,17]. The objective is concurrently estimating the robot pose and feature in certain environment.…”

Section: Adaptive Svsf-based Feature 2d Slam Algorithmmentioning

confidence: 99%

“…Since these tasks should be addressed at the same time, the definition of simultaneous localization and mapping (SLAM) is stated [6][7][8][9][10][11][12]. 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 [13,14]. Additionally, in the mobile robot application, the successful of solving SLAM problem can be validated root mean square error (RMSE) [15][16][17] calculated based on the different of the estimated and true value.…”

Section: Introductionmentioning

confidence: 99%

“…The easiness to apply extended kalman filter makes it has been widely used to solve in many different fields such as for the state and parameter estimation including SLAM, signal processing, fault detection and diagnosis and target tracking [29]. Nevertheless, it has many incompatibilities and difficulties such as the deviant solution from the state trajectory, less optimal state estimation and large estimation error due to the linearization process and computational cost [14,15,17]. These limitations make its practical application becomes limited nowadays.…”

Section: Introductionmentioning

confidence: 99%

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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: Adaptive Svsf-based Feature 2d Slam Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Initially, a classical EKF was estimated by using MAP creation, as mentioned on [21,22,23,24,25] . Assuming that, the unknown parameters are the process and measurement noise statistics with their covariance and , respectively.…”

Section: • Suboptimal Map Of Adaptive Ekfmentioning

confidence: 99%

“…Therefore, to find the conventional and efficient recursive form the simplification is needed. Note that the recursive update process only utilizes the estimated value at time k-1 and k, hence the simplification can be conducted by replacing with in (22) and (24) and with in (22) - (25). Therefore, the suboptimal of MAP noise estimator can be expressed as follows As can be analyzed from the sequence equations above that the estimated value of is not provided obviously by classical EKF.…”

Section: • Suboptimal Map Of Adaptive Ekfmentioning

confidence: 99%

A Mapaekf-Slam Algorithm With Recursive Mean and Covariance of Process and Measurement Noise Statistic

Suwoyo

Tian²,

Wang

et al. 2019

Sinergi

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The most popular filtering method used for solving a Simultaneous Localization and Mapping is the Extended Kalman Filter. Essentially, it requires prior stochastic knowledge both the process and measurement noise statistic. In order to avoid this requirement, these noise statistics have been defined at the beginning and kept to be fixed for the whole process. Indeed, it will satisfy the desired robustness in the case of simulation. Oppositely, due to the continuous uncertainty affected by the dynamic system under time integration, this manner is strongly not recommended. The reason is, improperly defined noise will not only degrade the filter performance but also might lead the filter to divergence condition. For this reason, there has been a strong manner well-termed as an adaptive-based strategy that commonly used to equip the classical filter for having an ability to approximate the noise statistic. Of course, by knowing the closely responsive noise statistic, the robustness and accuracy of an EKF can increase. However, most of the existed Adaptive-EKF only considered that the process and measurement noise statistic are characteristically zero-mean and responsive covariances. Accordingly, the robustness of EKF can still be enhanced. This paper presents a proposed method named as a MAPAEKF-SLAM algorithm used for solving the SLAM problem of a mobile robot, Turtlebot2. Sequentially, a classical EKF was estimated using Maximum a Posteriori. However, due to the existence of unobserved value, EKF was also smoothed one time based on the fixed-interval smoothing method. This smoothing step aims to keep-up the derivation process under MAP creation. Realistically, this proposed method was simulated and compared to the conventional one. Finally, it has been showing better accuracy in terms of Root Mean Square Error (RMSE) of both Estimated Map Coordinate (EMC) and Estimated Path Coordinate (EPC).

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An improved adaptive FastSLAM algorithm with time‐varying noise estimator

Karaçam

Navruz

2022

Asian Journal of Control

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In this study, an adaptive FastSLAM (AFastSLAM) algorithm, which is obtained by estimating the time-varying noise statistics and improving FastSLAM algorithm, is proposed. This improvement was accomplished by using maximum likelihood estimation and expectation maximization criterion and a one-step smoothing algorithm in importance sampling. In addition, innovation covariance estimation (ICE) method was used to prevent loss of positive definiteness of the process and measurement noise covariance matrices. The proposed method was compared with FastSLAM by calculating the root mean square error (RMSE) using different particle numbers at varying initial process and measurement noise values. Simulation studies have shown that AFastSLAM provides much more accurate, consistent, and successful estimates than FastSLAM for both robot and landmark positions.

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An AEKF-SLAM Algorithm with Recursive Noise Statistic Based on MLE and EM

Cited by 15 publications

References 18 publications

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

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

A Mapaekf-Slam Algorithm With Recursive Mean and Covariance of Process and Measurement Noise Statistic

An improved adaptive FastSLAM algorithm with time‐varying noise estimator

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