SLAM of AUV Based on the Combined EKF

Wang, Hongjian; Wang, Jing; Bian, Xinqian; Fu, Guixia

doi:10.3724/spj.1218.2012.00056

Cited by 6 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dimension-varying systems appear in nature and from engineering systems. For instance, In a genetic regulatory network, cells may die or birth at any time; in the internet or some other service-based networks, some users may join in or withdraw out from time to time; some mechanical systems, such as docking, undocking, departure, and joining of spacecrafts [50], [32], modeling of biological systems [49], [30], uncertain model identification [47], etc.…”

Section: B Potential Applicationsmentioning

confidence: 99%

Algebraic and geometric structures of matrices under STA and STP

Cheng

2016

2016 35th Chinese Control Conference (CCC)

View full text Add to dashboard Cite

A new mathematical structure, called the crossdimensional mathematics (CDM), is proposed. The CDM considered in this paper consists of three parts: hyper algebra, hyper geometry, and hyper Lie group/Lie algebra. Hyper algebra proposes some new algebraic structures such as hyper group, hyper ring, and hyper module over matrices and vectors with mixed dimensions (MVMDs). They have sets of classical groups, rings, and modules as their components and cross-dimensional connections among their components. Their basic properties are investigated. Hyper geometry starts from mixed dimensional Euclidian space, and hyper vector space. Then the hyper topological vector space, hyper inner product space, and hyper manifold are constructed. They have a joined cross-dimensional geometric structure. Finally, hyper metric space, topological hyper group and hyper Lie algebra are built gradually, and finally, the corresponding hyper Lie group is introduced. All these concepts are built over MVMDs, and to reach our purpose in addition to existing semi-tensor products (STPs) and semi-tensor additions (STAs), a couple of most general STP and STA are introduced. Some existing structures/results about STPs/STAs have also been resumed and integrated into this CDM.

show abstract

Section: B Potential Applicationsmentioning

confidence: 99%

Algebraic and geometric structures of matrices under STA and STP

Cheng

2016

2016 35th Chinese Control Conference (CCC)

View full text Add to dashboard Cite

show abstract

“…Suppose that the discrete state equation and the observation equation of the nonlinear system are shown as (5). On the basis of [24], the Sage-Husa estimator based adaptive EKF algorithm is shown aŝ…”

Section: Slam Algorithm Based On Adaptive Ckfmentioning

confidence: 99%

A SLAM Algorithm Based on Adaptive Cubature Kalman Filter

Sun

et al. 2014

Mathematical Problems in Engineering

View full text Add to dashboard Cite

We need to predict mathematical model of the system and a priori knowledge of the noise statistics when traditional simultaneous localization and mapping (SLAM) solutions are used. However, in many practical applications, prior statistics of the noise are unknown or time-varying, which will lead to large estimation errors or even cause divergence. In order to solve the above problem, an innovative cubature Kalman filter-based SLAM (CKF-SLAM) algorithm based on an adaptive cubature Kalman filter (ACKF) was established in this paper. The novel algorithm estimates the statistical parameters of the unknown system noise by introducing the Sage-Husa noise statistic estimator. Combining the advantages of the CKF-SLAM and the adaptive estimator, the new ACKF-SLAM algorithm can reduce the state estimated error significantly and improve the navigation accuracy of the SLAM system effectively. The performance of this new algorithm has been examined through numerical simulations in different scenarios. The results have shown that the position error can be effectively reduced with the new adaptive CKF-SLAM algorithm. Compared with other traditional SLAM methods, the accuracy of the nonlinear SLAM system is significantly improved. It verifies that the proposed ACKF-SLAM algorithm is valid and feasible.

show abstract

“…But the systematic error of UKF will lead to the non-positive definiteness and asymmetry of the covariance matrix. Therefore, the error of the gain matrix will be too large, which will lead to filter divergence [18,19]. Besides, the model error and calculation error of the filter cannot be avoided.…”

Section: Introductionmentioning

confidence: 99%

“…In order to enable SLAM to obtain some prior information, so as to further improve the accuracy that the SLAM's estimation of map features. Wang et al [18] proposes a new strategy. This strategy takes the observation set of the mobile robot at the last moment as the prior information set at the current moment.…”

Section: Introductionmentioning

confidence: 99%

A new PHD-SLAM method based on memory attenuation filter

Zhang

Yang

2021

Meas. Sci. Technol.

View full text Add to dashboard Cite

Aiming at the problem that the low signal-to-noise ratio in the complex indoor environment will lead to the complex data association and low accuracy of the simultaneous localization and mapping (SLAM) method, a PHD-SLAM method based on the memory attenuation (MA) filter and mixed newborn maps information (MBMA-PHD-SLAM) is proposed in this paper. First of all, this method based on a probability hypothesis density (PHD) filter. Therefore, this method avoids data association and solves the problem of high computational complexity. Besides, the general PHD-SLAM method tends to cause the filter to diverge when the indoor signal-to-noise ratio is low. Therefore, MA filter is combined into PHD-SLAM. This method can reduce the influence of old data on SLAM, which improves the filtering divergence problem and improves the accuracy of SLAM. What's more, since conventional SLAM algorithms usually have a problem of lack of prior information and in order to further improve the accuracy of SLAM on the basis of the above method, a method of mixed newborn maps information is proposed to solve this problem. Finally, the experiment mainly compares the method in this paper with the classic Rao-Blackwellised (RB) implementation of the PHD-SLAM (RB-PHD-SLAM). The results show that this method outperforms the PHD-SLAM method in terms of estimating map features and localization accuracy. The method in this paper effectively improves the ability of the mobile robot to explore unknown indoor environments.

show abstract

SLAM of AUV Based on the Combined EKF

Cited by 6 publications

References 0 publications

Algebraic and geometric structures of matrices under STA and STP

Algebraic and geometric structures of matrices under STA and STP

A SLAM Algorithm Based on Adaptive Cubature Kalman Filter

A new PHD-SLAM method based on memory attenuation filter

Contact Info

Product

Resources

About