The application of adaptive extended Kalman filter in mobile robot localization

Pi, Yiming; Yuan, Qiangqiang; Benfa, Zhang

doi:10.1109/ccdc.2016.7531952

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…Different modifications have been conducted to enhance the adaptability of the EKF, for example, its adaptive filtering. This type of filtering is based on the determination of the dynamic system's statistical parameters according to the system's behavior during data processing [14,15]. One of the problems associated with mobile robot localization is error accumulation; to solve or minimize this issue, [15] proposes a variation of the EKF known as AEKF (Adaptive Extended Kalman Filter).…”

Section: Kalman Filter Modificationsmentioning

confidence: 99%

“…This type of filtering is based on the determination of the dynamic system's statistical parameters according to the system's behavior during data processing [14,15]. One of the problems associated with mobile robot localization is error accumulation; to solve or minimize this issue, [15] proposes a variation of the EKF known as AEKF (Adaptive Extended Kalman Filter). This variant employs Taylor's series in sampling time as an estimator of variable noise over time, and the Sage-Husa method to estimate the observation noise in real time.…”

Section: Kalman Filter Modificationsmentioning

confidence: 99%

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Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation

Urrea

Agramonte

2021

Journal of Sensors

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Due to its widespread application in the robotics field, the Kalman filter has received increased attention from researchers. This work reviews some of the modifications conducted on to this algorithm over the last years. Problems such as the consistency, convergence, and accuracy of the filter are also dealt with. Sixty years after its creation, the Kalman filter is still used in autonomous navigation processes, robot control, and trajectory tracking, among other activities. The filter is not only restricted to robotics but is also present in different fields, such as economics and medicine. In addition, the characteristics of each modification on this filter are analyzed and compared.

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Section: Kalman Filter Modificationsmentioning

confidence: 99%

Section: Kalman Filter Modificationsmentioning

confidence: 99%

Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation

Urrea

Agramonte

2021

Journal of Sensors

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“…we have the following equation 18Next, by supposing that (19) Then (18) can be simplified as follows (20) At this point, the estimated unknown parameter can be calculated by taking the logarithm of the objective function , calculating the first derivative of logarithm with respect to , , , and and equating its derived to be zero. These steps can be derived as follows.…”

Section: • Suboptimal Map Of Adaptive Ekfmentioning

confidence: 99%

“…It aims to reduce the mismatch between the theoretical and actual covariance of the innovation sequences. Moreover, Yuzhen, Quande, and Benfa have proposed AEKF by using the Sage-Husa time-varying noise estimator and Taylor series of sampling time in AEKF to estimate observation noise in real-time [19] . It aims to overcome the linearization error and enhance environmental adaptability.…”

Section: Introductionmentioning

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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“…The adaptive Augmented Extended Kalman Filter (A.E.K.F.) algorithm has been proposed [8] to solve the accumulation of errors in the process of locating autonomous mobile robots indoors. Convergence and complexity are analyzed, and the experiments carried out proved that the A.E.K.F.…”

Section: Introductionmentioning

confidence: 99%