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).

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Suwoyo, H., Tian, Y., Deng, C., & Adriansyah, A. (2018). Improving a Wall-Following Robot Performance with a PID-Genetic Algorithm Controller. Proceeding of the Electrical Engineering Computer Science and Informatics, 5(1), 314-318.

Adriansyah, A., Suwoyo, H., & Tian, Y. (2019). Jurnal Teknologi IMPROVING ROBOT. 3, 119–126.

Afshari, H. H., Gadsden, S. A., & Habibi, S. (2017). Gaussian filters for parameter and state estimation: A general review of theory and recent trends. Signal Processing. https://doi.org/10.1016/j.sigpro.2017.01.001

Ahmed, R., El Sayed, M., Gadsden, S. A., Tjong, J., & Habibi, S. (2016). Artificial neural network training utilizing the smooth variable structure filter estimation strategy. Neural Computing and Applications, 27(3), 537–548. https://doi.org/10.1007/s00521-015-1875-2

Akhlaghi, S., Zhou, N., & Huang, Z. (2018). Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation. IEEE Power and Energy Society General Meeting, 2018-January, 1–5. https://doi.org/10.1109/PESGM.2017.8273755

Al-Shabi, M., & Habibi, S. (2011). Iterative smooth variable structure filter for parameter estimation. ISRN Signal Processing, 2011(1). https://doi.org/10.5402/2011/725108

Bailey, T. (2002). Mobile Robot Localisation and Mapping in Extensive Outdoor Environments. Philosophy, 31(August), 212. https://doi.org/10.1016/S0921-8890(99)00078-0

Bailey, T., & Durrant-whyte, H. (n.d.). Simultaneous Localisation and Mapping ( SLAM ): Part II State of the Art. 1–10.

Bar-Shalon, Y., Li, X.-R., & Kirubarajan, T. (n.d.). Estimation with Applications to Tracking and Navigation.

Benzerrouk, H., Nebylov, A., & Salhi, H. (2016). Quadrotor UAV state estimation based on High-Degree Cubature Kalman filter. IFAC-PapersOnLine. https://doi.org/10.1016/j.ifacol.2016.09.060

Chen, S., Shi, Z., & Ding, J. (2017). Application of the 2nd-order Smooth Variable Structure Filter algorithm for SINS initial alignment. 2017 Forum on Cooperative Positioning and Service, CPGPS 2017, (1), 43–49. https://doi.org/10.1109/CPGPS.2017.8075095

Choset, H., Lynch, K., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L., & Thrun, S. (2006). PRINCIPLES OF ROBOT MOTION, Theory, Algorithms and Implementations, by Howie Choset et al., MIT Press, 2005. xix + 603 pp., index, ISBN 0-262-03327-5, 433 references (Hb. £38.95) . Robotica, 24(2), 271–271. https://doi.org/10.1017/s0263574706212803

Demim, F., Nemra, A., & Louadj, K. (2016). Robust SVSF-SLAM for Unmanned Vehicle in Unknown Environment. IFAC-PapersOnLine. https://doi.org/10.1016/j.ifacol.2016.10.585

Fethi, D., Nemra, A., Louadj, K., & Hamerlain, M. (2018). Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control. Advances in Mechanical Engineering, 10(1), 1–25. https://doi.org/10.1177/1687814017736653

Gadsden, S. A., Al-Shabi, M., Arasaratnam, I., & Habibi, S. R. (2014). Combined cubature Kalman and smooth variable structure filtering: A robust nonlinear estimation strategy. Signal Processing, 96 (PART B), 290–299. https://doi.org/10.1016/j.sigpro.2013.08.015

Gadsden, S. Andrew, & Afshari, H. H. (2015). A review of smooth variable structure filters: Recent advances in theory and applications. ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), 4A-2015. https://doi.org/10.1115/IMECE2015-50966

Gadsden, S. Andrew, Habibi, S. R., Dunne, D., & Kirubarajan, T. (2011). Combined particle and smooth variable structure filtering for nonlinear estimation problems. Fusion 2011 - 14th International Conference on Information Fusion, 1552–1559.

Gadsden, S. Andrew, & Lee, A. S. (2017). Advances of the smooth variable structure filter: square-root and two-pass formulations. Journal of Applied Remote Sensing. https://doi.org/10.1117/1.jrs.11.015018

Gadsden, Stephen Andrew. (2011). Smooth Variable Structure Filter Theory and Application.pdf. McMaster University.

Gao, B., Gao, S., Gao, L., & Hu, G. (2016). An adaptive UKF for nonlinear state estimation via maximum likelihood principle. ICEIEC 2016 - Proceedings of 2016 IEEE 6th International Conference on Electronics Information and Emergency Communication, (4), 117–120. https://doi.org/10.1109/ICEIEC.2016.7589701

Gao, B., Gao, S., Hu, G., Zhong, Y., & Gu, C. (2018). Maximum likelihood principle and moving horizon estimation based adaptive unscented Kalman filter. Aerospace Science and Technology. https://doi.org/10.1016/j.ast.2017.12.007

Gao, W., Li, J., Zhou, G., & Li, Q. (2015). Adaptive Kalman filtering with recursive noise estimator for integrated SINS/DVL systems. Journal of Navigation, 68(1), 142–161. https://doi.org/10.1017/S0373463314000484

Gao, Z., Mu, D., Zhong, Y., Gu, C., & Ren, C. (2019). Adaptively random weighted cubature kalman filter for nonlinear systems. Mathematical Problems in Engineering, 2019. https://doi.org/10.1155/2019/4160847

Grisetti, G. (2004). Towards a PhD Thesis on Simultaneous Localization and Mapping. 1–38. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.106.4683&rep=rep1&type=pdf

Habibi, B. S. (2007). Structure Filter. Proceedings of the IEEE, 95(5).

He, J., Chen, Y., Zhang, Z., Yin, W., & Chen, D. (2018). A hybrid adaptive unscented Kalman filter algorithm. International Journal for Engineering Modelling, 31(3), 51–65. https://doi.org/10.31534/engmod.2018.3.ri.04d

Huang, Y., Zhang, Y., Xu, B., Wu, Z., & Chambers, J. A. (2018). A New Adaptive Extended Kalman Filter for Cooperative Localization. IEEE Transactions on Aerospace and Electronic Systems, 54(1), 353–368. https://doi.org/10.1109/TAES.2017.2756763

Kim, K. H., Lee, J. G., & Park, C. G. (2009). Adaptive two-stage extended kalman filter for a fault-tolerant INS-GPS loosely coupled system. IEEE Transactions on Aerospace and Electronic Systems, 45(1), 125–137. https://doi.org/10.1109/TAES.2009.4805268

Kim, T., Wang, Y., Sahinoglu, Z., Wada, T., Hara, S., & Qiao, W. (2014). State of charge estimation based on a realtime battery model and iterative smooth variable structure filter. 2014 IEEE Innovative Smart Grid Technologies - Asia, ISGT ASIA 2014, 132–137. https://doi.org/10.1109/ISGT-Asia.2014.6873777

LaViola, J. J. (2003). A Comparison of Unscented and Extended Kalman Filtering for Estimating Quaternion Motion. Proceedings of the American Control Conference, 3, 2435–2440.

Levitan, E., & Herman, G. T. (1987). A Maximum A Posteriori Probability Expectation Maximization Algorithm for Image Reconstruction in Emission Tomography. IEEE Transactions on Medical Imaging, 6(3), 185–192. https://doi.org/10.1109/TMI.1987.4307826

Li, Z., Yang, W., & Ding, D. (2017). Time-Varying Noise Statistic Estimator Based Adaptive Simplex Cubature Kalman Filter. Mathematical Problems in Engineering, 2017. https://doi.org/10.1155/2017/5349879

Mohamed, A. H., & Schwarz, K. P. (1999). Adaptive Kalman filtering for INS/GPS. Journal of Geodesy, 73(4), 193–203. https://doi.org/10.1007/s001900050236

Outamazirt, F., Fu, L., Lin, Y., & Abdelkrim, N. (2016). A new SINS/GPS sensor fusion scheme for UAV localization problem using nonlinear SVSF with covariance derivation and an adaptive boundary layer. Chinese Journal of Aeronautics. https://doi.org/10.1016/j.cja.2016.02.005

Shao, X., He, B., Guo, J., & Yan, T. (2016). The application of AUV navigation based on adaptive extended Kalman filter. OCEANS 2016 - Shanghai. https://doi.org/10.1109/OCEANSAP.2016.7485592

Suwoyo, H., Deng, C., Tian, Y., & Adriansyah, A. (2018). Improving a wall-following robot performance with a PID-genetic algorithm controller. International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2018-Octob, 314–318. https://doi.org/10.1109/EECSI.2018.8752907

Thrun, S. (2002). Probabilistic robotics. Communications of the ACM, 45(3), 52–57. https://doi.org/10.1145/504729.504754

Tian, Y., Suwoyo, H., Wang, W., & Li, L. (2019). An ASVSF-SLAM Algorithm with Time-Varying Noise Statistics Based on MAP Creation and Weighted Exponent. Mathematical Problems in Engineering, 2019, 28–34. https://doi.org/10.1155/2019/2765731

Tian, Y., Suwoyo, H., Wang, W., Mbemba, D., & Li, L. (2019). An AEKF-SLAM Algorithm with Recursive Noise Statistic Based on MLE and EM. Journal of Intelligent and Robotic Systems: Theory and Applications. https://doi.org/10.1007/s10846-019-01044-8

Wan, E. A., & Van Der Merwe, R. (2000). The unscented Kalman filter for nonlinear estimation. IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, AS-SPCC 2000, 153–158. https://doi.org/10.1109/ASSPCC.2000.882463

Wang, Hairong, Deng, Z., Feng, B., Ma, H., & Xia, Y. (2017). An adaptive Kalman filter estimating process noise covariance. Neurocomputing. https://doi.org/10.1016/j.neucom.2016.10.026

Wang, Hongjian, Fu, G., Li, J., Yan, Z., & Bian, X. (2013). An adaptive UKF based SLAM method for unmanned underwater vehicle. Mathematical Problems in Engineering, 2013. https://doi.org/10.1155/2013/605981

Wang, X., Song, B., Liang, Y., & Pan, Q. (2017). EM-based adaptive divided difference filter for nonlinear system with multiplicative parameter. International Journal of Robust and Nonlinear Control, 27(13), 2167–2197. https://doi.org/10.1002/rnc.3674

Woo, R., Yang, E. J., & Seo, D. W. (2019). A fuzzy-innovation-based adaptive Kalman filter for enhanced vehicle positioning in dense urban environments. Sensors (Switzerland), 19(5). https://doi.org/10.3390/s19051142

Yang, J. N., Pan, S., & Huang, H. (2007). An adaptive extended Kalman filter for structural damage identifications II: Unknown inputs. Structural Control and Health Monitoring, 14(3), 497–521. https://doi.org/10.1002/stc.171

Yang, Jann N., Lin, S., Huang, H., & Zhou, L. (2006). An adaptive extended Kalman filter for structural damage identification. Structural Control and Health Monitoring, 13(4), 849–867. https://doi.org/10.1002/stc.84

Yi, B., Kang, L., Tao, S., Zhao, X., & Jing, Z. (2013). Adaptive two-stage extended kalman filter theory in application of sensorless control for permanent magnet synchronous motor. Mathematical Problems in Engineering, 2013. https://doi.org/10.1155/2013/974974

Yong, S., Chongzhao, H., & Yongqi, L. (2009). Adaptive UKF for target tracking with unknown process noise statistics. 2009 12th International Conference on Information Fusion, FUSION 2009, (1), 1815–1820.

Yu, F., Sun, Q., Lv, C., Ben, Y., & Fu, Y. (2014). A SLAM algorithm based on adaptive cubature Kalman filter. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/171958

Zeng, Z., Zhang, S., Xing, Y., & Cao, X. (2014). Robust adaptive filter for small satellite attitude estimation based on magnetometer and gyro. Abstract and Applied Analysis, 2014. https://doi.org/10.1155/2014/159149

Downloads

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

Issue

Section

Mechatronics and Automation Engineering