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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 33, Number 8(1) (2021)
Copyright(C) MYU K.K.
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  This work is licensed under a Creative Commons Attribution 4.0 International License. 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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