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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials, Volume 33, Number 8(1) (2021)
Copyright(C) MYU K.K.
pp. 2591-2606
S&M2641 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2021.3387
Published: August 10, 2021

Simultaneous Localization and Mapping Method Based on Improved Cubature Kalman Filter [PDF]

Chaoyang Chen, Qi He, Qiubo Ye, Guangsong Yang, and Cheng-Fu Yang

(Received March 23, 2021; Accepted June 2, 2021)

Keywords: simultaneous localization and mapping (SLAM), cubature Kalman filter, error covariance matrix, root mean square error (RMSE)

Toward solving some of the problems of low precision, poor stability, and complex calculation in the simultaneous localization and mapping (SLAM) of mobile robots, an improved cubature Kalman filter SLAM (ICKF-SLAM) algorithm based on the cubature Kalman filter SLAM (CKF-SLAM) algorithm is proposed. Firstly, the error covariance matrix of the state vector is obtained through the motion model and observation model of the mobile robot. Then, the information matrix is obtained by the inverse operation, and the information state vector is updated in the prediction and update phases. The proposed method reduces the computational complexity and improves the accuracy of the algorithm. Simulation results show that compared with CKF-SLAM, the root mean square error of ICKF-SLAM is reduced by 11.8%.

Corresponding author: Guangsong Yang, Cheng-Fu Yang


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Cite this article
Chaoyang Chen, Qi He, Qiubo Ye, Guangsong Yang, and Cheng-Fu Yang, Simultaneous Localization and Mapping Method Based on Improved Cubature Kalman Filter, Sens. Mater., Vol. 33, No. 8, 2021, p. 2591-2606.



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