ADAPTIVE DEVELOPMENT OF SVSF FOR A FEATURE-BASED SLAM ALGORITHM USING MAXIMUM LIKELIHOOD ESTIMATION AND EXPECTATION MAXIMIZATION

Authors

  • Heru Suwoyo Department of Electrical Engineering, Universitas Mercu Buana,Jakarta, 11650, Indonesia
  • Yingzhong Tian School of Mechatronic Engineering and Automation, Shanghai University, China
  • Wenbing Wang Mechanical and Electrical Engineering School, Shenzhen Polytechnic, China
  • Long Li School of Mechatronic Engineering and Automation, Shanghai University, China https://orcid.org/0000-0003-1973-8550
  • Andi Adriansyah Department of Electrical Engineering, Universitas Mercu Buana, Indonesia https://orcid.org/0000-0002-3911-7455
  • Fengfeng Xi Department of Mechanical, Aerospace, and Industrial Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
  • Guangjie Yuan School of Mechatronic Engineering and Automation, Shanghai University, China

DOI:

https://doi.org/10.31436/iiumej.v22i1.1403

Keywords:

SLAM, ASVSF, MLE, EM, ICE

Abstract

ABSTRACT:  The smooth variable structure filter (SVSF) has been considered as the robust estimator. Like other filters, the SVSF needs an accurate system model and known noise statistics to approximate the posterior state. Unfortunately, the system cannot be accurately modeled, and the noise statistic is unknown in the real application. For these reasons, the performance of SVSF might be decreased or even led to divergence. Therefore, the enhancement of SVSF is required. This paper presents an Adaptive SVSF. Initially, SVSF is smoothed. To provide the ability to estimate the noise statistic, ASVSF is then derived based on maximum likelihood estimation (MLE) and expectation-maximization (EM). Additionally, the unbiased noise statistic is also approached. However, its covariance is complicatedly formulated. It might cause a negative definite symmetric matrix. Therefore, it is tuned based on the innovation covariance estimator (ICE). The ASVSF is designed to solve the online problem of Simultaneous Localization and Mapping (SLAM). Henceforth, it is termed as the ASVSF-SLAM algorithm. The proposed algorithm showed better accuracy and stability compared to the conventional algorithm in terms of root mean square error (RMSE) for both Estimated Path Coordinate (EPC) and Estimated Map Coordinate (EMC).

ABSTRAK: Penapis struktur bolehubah lembut (SVSF) telah dianggap sebagai penganggar teguh. Seperti penapis lain, SVSF memerlukan model sistem yang tepat dan statistik hingar yang diketahui bagi menganggar keadaan posterior. Malangnya, sistem tidak dapat dimodelkan dengan tepat dan statistik hingar tidak diketahui dalam aplikasi sebenar. Atas sebab-sebab ini, prestasi SVSF mungkin berkurangan, bahkan berbeza. Oleh itu, memperbaharui SVSF adalah perlu. Kajian ini adalah mengenai SVSF Mudah Suai. Pada awalnya, SVSF dilembutkan. Bagi menyediakan keupayaan anggaran statistik hinggar, ASVSF dihasilkan terlebih dahulu berdasarkan anggaran kemungkinan maksimum (MLE) dan maksimum-harapan (EM). Tambahan, statistik hinggar yang tidak berat sebelah juga dibuat. Walau bagaimanapun, rumusan formula kovarians ini adalah kompleks. Ini mungkin menyebabkan matriks simetri menjadi negatif. Oleh itu, ia diselaraskan berdasarkan penganggar kovarians inovasi (ICE). ASVSF dibina bagi menyelesaikan masalah dalam talian Penempatan dan Pemetaan Serentak (SLAM) dalam talian. Oleh itu, ia disebut sebagai algoritma ASVSF-SLAM. Algoritma yang dicadangkan ini menunjukkan ketepatan dan kestabilan yang lebih baik berbanding algoritma konvensional dari segi ralat punca min kuasa dua (RMSE) bagi kedua-dua Koordinat Anggaran Laluan (EPC) dan Anggaran Koordinat Peta (EMC).

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Published

2020-01-04

How to Cite

Suwoyo, H., Tian, Y. ., Wang, W. ., Li, L. ., Adriansyah, A. ., Xi, F., & Yuan, G. . (2020). ADAPTIVE DEVELOPMENT OF SVSF FOR A FEATURE-BASED SLAM ALGORITHM USING MAXIMUM LIKELIHOOD ESTIMATION AND EXPECTATION MAXIMIZATION. IIUM Engineering Journal, 22(1), 269–286. https://doi.org/10.31436/iiumej.v22i1.1403

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Mechatronics and Automation Engineering

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