S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
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Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Simultaneous Localization and Map Method Based on Improved Cubature Kalman Filter

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

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

Keywords: simultaneous localization and map construction (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 map construction (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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