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SLAM with Improved Schmidt Orthogonal Unscented Kalman Filter

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  • Robot and Applications
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Abstract

Simultaneous localization and mapping (SLAM) is a momentous topic for robot navigation to explore uncharted environment. To enhance the accuracy and efficiency, an improved Schmidt orthogonal unscented Kalman filter (ISOUKF) based SLAM algorithm is proposed in this paper. First, based on the Schmidt orthogonal transform (SOT) sampling, a modified unscented Kalman filter (UKF) algorithm is presented. Then, an adaptive fading factor is derived using the strong tracking algorithm, and it is introduced into the prediction covariance to improve tracking ability and accuracy. Next, the Schmidt orthogonal unscented Kalman filter is improved with square root filter to raise the efficiency of SLAM algorithm. Finally, the ISOUKF algorithm is proposed to complete the robot tracking in SLAM. The proposed algorithm provides a high precision robot tracking for SLAM and decreases the computational cost to some extent. Experiment results verify the superiority of the proposed algorithm.

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Funding

This work was supported by National Natural Science Foundation of China (Nos. 61771091, 61871066), National High Technology Research and Development Program (863 Program) of China (No. 2015AA016306), Natural Science Foundation of Liaoning Province of China (No. 20170540159), and Fundamental Research Funds for the Central Universities of China (No. DUT17LAB04).

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology (DUT), No. 2 Linggong Road, Ganjingzi District, Dalian, 116024, China

    Ming Tang, Zhe Chen & Fuliang Yin

Authors
  1. Ming Tang
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  2. Zhe Chen
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  3. Fuliang Yin
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Corresponding author

Correspondence to Fuliang Yin.

Additional information

Ming Tang received his B.S. degree in electronic information engineering, and an M.S. degree in optics from Liaoning Normal University (LNNU), Dalian, China, in 2012 and 2016, respectively. He is currently working toward a Ph.D. degree in signal and information processing in the School of Information and Communication Engineering, Dalian University of Technology (DUT), Dalian, China. His research interests include image processing, robot localization, and tracking.

Zhe Chen received his B.S. degree in electronic engineering, his M.S. and Ph.D. degrees in signal and information processing from Dalian University of Technology (DUT), Dalian, China, in 1996, 1999, and 2003, respectively. He joined the Department of Electronic Engineering, DUT, as a Lecturer in 2002, became an Associate Professor in 2006, and has been a Professor since 2017. His research interests include speech processing, image processing, and wideband wireless communication.

Fuliang Yin was born in Fushun city, Liaoning province, China, in 1962. He received his B.S. degree in electronic engineering and an M.S. degree in communications and electronic systems from Dalian University of Technology (DUT), Dalian, China, in 1984 and 1987, respectively. He joined the Department of Electronic Engineering, DUT, as a Lecturer in 1987 and became an Associate Professor in 1991. He has been a Professor at DUT since 1994, and the Dean of School of Electronic and Information Engineering, DUT, from 2000 to 2009. His research interests include speech processing, image processing, and broadband wireless communication.

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Tang, M., Chen, Z. & Yin, F. SLAM with Improved Schmidt Orthogonal Unscented Kalman Filter. Int. J. Control Autom. Syst. 20, 1327–1335 (2022). https://doi.org/10.1007/s12555-020-0896-5

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  • DOI: https://doi.org/10.1007/s12555-020-0896-5

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