A novel STSOSLAM algorithm based on strong tracking second order central difference Kalman filter

Dai, Jiahui; Li, Xiaobo; Wang, Kequan

doi:10.1016/j.robot.2019.03.006

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…A recent approach strong tracking second-order (STSO) central difference SLAM is presented in [49] which it is based on the tracking second-order central difference KF. The proposed procedure gathers the second-order central differential filter (SOCDF), strong tracking filter (STF), and PF.…”

Section: Comparison Of the Proposed And Other Algorithmsmentioning

confidence: 99%

Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

Ullah

Zhang

et al. 2020

Wireless Communications and Mobile Computing

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For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

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Section: Comparison Of the Proposed And Other Algorithmsmentioning

confidence: 99%

Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

Ullah

Zhang

et al. 2020

Wireless Communications and Mobile Computing

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“…(4) 𝜌 s : its general range is 0.9∼0.95. 15 It can be seen from ( 90) that if the value of 𝜌 s is smaller, the filter will overemphasize the role of εk εT k , which is easy to cause the over adjustment of the time-varying fading factor to the state estimation; on the contrary, if it is larger, the current observation information will be weakened correspondingly, and the tracking performance of the filter cannot be optimized. Therefore, in our study, we take its intermediate value, that is, 𝜌 s = 0.95.…”

Section: F I G U R E 3 Flight Models Of Attitude Sensorsmentioning

confidence: 99%

An improved central difference Kalman filter for satellite attitude estimation with state mutation

Zhang

et al. 2022

Intl J Robust & Nonlinear

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There are three problems in applying central difference Kalman filter (CDKF) to attitude estimation of low-cost satellite: (1) the negative definiteness and asymmetry of the covariance matrix caused by limited computing power and storage space of the onboard microprocessor may result in calculation divergence, (2) the system model uncertainty including model mismatch and state mutation caused by environmental disturbances from inside and outside the satellite will lead to filter divergence, (3) the addition operation on quaternions violates the normalization constraint, while enforced unitization will introduce approximation errors. To solve the above problems, this article proposes the multiplicative quaternion square root strong tracking CDKF (MQSRSTCDKF), in which three improvements to basic CDKF are made: (1) square roots of covariance matrices are adopted for information propagation in place of covariance matrices themselves, which always guarantees the symmetry and positive definiteness of covariance matrices, (2) multiple fading factors are employed to adjust square root of the covariance matrix of predicted state variables, and then adjust the gain matrix to make the filter attach more weight to sensor measurements than to corrupt state predictions caused by model uncertainties, (3) multiplication operation on quaternions is conducted instead of the addition one, which makes the normalization constraint on quaternions naturally satisfied without enforced unitization. Numerical simulation based on raw telemetry data from the on-orbit CubeSat NJUST-1 shows that MQSRSTCDKF has higher attitude estimation accuracy, faster convergence speed, and stronger robustness to filter divergence and calculation divergence than basic CDKF.

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“…Other research has suggested using this technique to improve the EKF, which has continued to evolve, leading to the creation of the SOCDKF (Second Order Central Difference Kalman Filter). The latter employs a second-order truncated Stirling interpolation to estimate posterior covariance, so that the approximation accuracy of the posterior mean and autocovariance can reach the first two elements of the UT transform [24].…”

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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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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A novel STSOSLAM algorithm based on strong tracking second order central difference Kalman filter

Cited by 11 publications

References 13 publications

Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

An improved central difference Kalman filter for satellite attitude estimation with state mutation

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

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