A SLAM algorithm based on the central difference Kalman filter

Zhu, Jihua; Zheng, Nanning; Yuan, Zejian; Zhang, Qiang; Zhang, Xuetao; He, Yixin

doi:10.1109/ivs.2009.5164264

Cited by 37 publications

(25 citation statements)

References 13 publications

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“…The Central Difference Kalman Filter (CDKF), described in [40]- [42], was derived in two major stages. The first stage was to linearize the system model using TSA.…”

Section: The Central Difference Kalman Filtermentioning

confidence: 99%

Sigma-Point Filters in Robotic Applications

Al-Shabi¹

2015

ICA

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Sigma-Point Kalman Filters (SPKFs) are popular estimation techniques for high nonlinear system applications. The benefits of using SPKFs include (but not limited to) the following: the easiness of linearizing the nonlinear matrices statistically without the need to use the Jacobian matrices, the ability to handle more uncertainties than the Extended Kalman Filter (EKF), the ability to handle different types of noise, having less computational time than the Particle Filter (PF) and most of the adaptive techniques which makes it suitable for online applications, and having acceptable performance compared to other nonlinear estimation techniques. Therefore, SPKFs are a strong candidate for nonlinear industrial applications, i.e. robotic arm. Controlling a robotic arm is hard and challenging due to the system nature, which includes sinusoidal functions, and the dependency on the sensors' number, quality, accuracy and functionality. SPKFs provide with a mechanism that reduces the latter issue in terms of numbers of required sensors and their sensitivity. Moreover, they could handle the nonlinearity for a certain degree. This could be used to improve the controller quality while reducing the cost. In this paper, some SPKF algorithms are applied to 4-DOF robotic arm that consists of one prismatic joint and three revolute joints (PRRR). Those include the Unscented Kalman Filter (UKF), the Cubature Kalman Filter (CKF), and the Central Differences Kalman Filter (CDKF). This study gives a study of those filters and their responses, stability, robustness, computational time, complexity and convergences in order to obtain the suitable filter for an experimental setup.

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“…The Central Difference Kalman Filter (CDKF), described in [40]- [42], was derived in two major stages. The first stage was to linearize the system model using TSA.…”

Section: The Central Difference Kalman Filtermentioning

confidence: 99%

Sigma-Point Filters in Robotic Applications

Al-Shabi¹

2015

ICA

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“…Moreover, instead of taking the square root of state covariance in the UKF, state covariance will be propagated directly in the SR-CDKF, which avoids the need to refactorize in each time step [18]. Additionally, SR-CDKF has a good advantage over UKF such that SR-CDKF is more adaptive than UKF [19]. In Gaussian distributions, the optimal value of scalar scaling parameters should be appropriately selected in the UKF for different applications.…”

Section: Introductionmentioning

confidence: 99%

Square root central difference-based FastSLAM approach improved by differential evolution

Ankishan¹,

Arı²,

Tartan³

et al. 2016

Turk J Elec Eng & Comp Sci

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“…Like the UKF, the DDF generates several points about the mean based on varying the covariance matrix along each dimension. A slight difference from the UKF is in that the DDF evaluates a nonlinear function at two different points for each dimension of the state vector that are divided proportionally by the chosen step size [8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the DDF provides a faster processing speed than the UKF because it does not need to predict forward every positive and negative sigma point in separate stages from when the measurement prediction, measurement prediction covariance and cross-covariance are solved. Also, the DDF impose less system memory requirement [9,10].…”

Section: Introductionmentioning

confidence: 99%