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SLAM estimation method for uncertain model noise parameters

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Abstract

It is difficult to accurately obtain the statistical parameters of mobile robots motion parameters for its mobility. And it is also the same in observation system because of the environmental variability, which would cause the SLAM system noise statistical parameters uncertainty, and decrease the filtering performance. A SLAM adaptive filtering algorithm with noise statistical characteristics estimation is proposed, when the noise statistical parameters are constant but unknown, three additional maximum posteriori estimators are constructed, and adaptive CKF SLAM algorithm are designed. Compared with the standard CKF SLAM algorithm, the results show that under the uncertainty noise parameters, if the noise variance is Gaussian white noise distribution, the designed adaptive CKF SLAM with three maximum posterior can well estimate the state, track noise variance, and handle the problem of nonlinear systems. The simulation results verify the effectiveness of the proposed SLAM estimation method with the uncertainty of the model noise parameters.

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References

  1. Subrahmanya, N., Shin, Y.C.: Adaptive divided difference filtering for simultaneous state and parameter estimation. Automatica 45(7), 1686–1693 (2009)

    Article  MathSciNet  Google Scholar 

  2. Zia, A., Kirubarajan, T., Reilly, J.P., et al.: An em algorithm for nonlinear state estimation with model uncertainties. IEEE Trans. Signal Process. 56(3), 921–936 (2008)

    Article  MathSciNet  Google Scholar 

  3. Zhou, W., Qiao, X., Ji, Y., et al.: An innovation and residual-based adaptive ukf algorithm. J. Astronaut. 31(7), 1798–1804 (2010)

    Google Scholar 

  4. Hajiyev, C.: Adaptive filtration algorithm with the filter gain correction applied to integrated INS/Radar altimeter. Proc. Inst. Mech. Eng. Part G 221(5), 2041–3025 (2007)

    Article  Google Scholar 

  5. Wang, X.X., Zhao, L., Xia, Q.X., et al.: Strong tracking filter based on unscented transformation. Control Decis. 25(7), 1063–1068 (2010)

    MathSciNet  Google Scholar 

  6. Gauvain, J.L., Lee, C.H.: Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEE Trans. Speech Audio Process. 2(2), 291–298 (1994)

    Article  Google Scholar 

  7. Yang, Z., Pan, J., Cai, L.: Adaptive clock skew estimation with interactive multi-model Kalman filters for sensor networks. In: IEEE International Conference on Communications (ICC), 2010, 1–5 (2010)

  8. Tudoroiu, N., Khorasani, K.: Fault detection and diagnosis for satellite’s attitude control system (ACS) using an interactive multiple model (IMM) approach. In: Proceedings of 2005 IEEE Conference on Control Applications. 1287–1292 (2005)

  9. Yu, H., Song, S., Wang, S.: Interaction cubature Kalman filter and its application. Control Decis. 30(09), 1660–1666 (2015)

    Google Scholar 

  10. Lin, Z., Xiao-Xu, W., Ming, S., et al.: Adaptive UKF filtering algorithm based on maximum a posterior estimation and exponential weighting. Acta Automatica Sinica 36(7), 1007–1019 (2010)

    Article  MathSciNet  Google Scholar 

  11. Ying-chun, S.: Current statistics model and adaptive kalman filter in gps navigation. J. Hunan Inst. Hum. Sci. Technol. 05, 12–14 (2005)

    Google Scholar 

  12. Bhaumik, S.: Cubature quadrature Kalman filter. IET Signal Process. 7(7), 533–541 (2013)

    Article  MathSciNet  Google Scholar 

  13. Wang, S., Feng, J., Chi, K.T.: Spherical simplex-radial cubature Kalman filter. IEEE Signal Process. Lett. 21(1), 43–46 (2014)

    Article  Google Scholar 

  14. Julier, S.J.: The scaled unscented transformation. American Control Conference, 2002. In: Proceedings of the 2002. IEEE, 6, 4555–4559 (2002)

  15. Nrgaard, M., Poulsen, N.K., Ravn, O.: New developments in state estimation for nonlinear systems. Automatica 36(11), 1627–1638 (2000)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Program No.11747119), and supported by the Program for Innovative Science and Research Team of Xi’an Technological University.

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Authors and Affiliations

  1. School of Electronic Information Engineering, Xi’an Technological University, Shaanxi, China

    Junchai Gao & Keding Yan

  2. Information Technology Center, Xi’an Technological University, Shaanxi, China

    Bing Han

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  1. Junchai Gao
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  2. Keding Yan
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  3. Bing Han
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Correspondence to Junchai Gao.

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Gao, J., Yan, K. & Han, B. SLAM estimation method for uncertain model noise parameters. Cluster Comput 22 (Suppl 4), 9425–9434 (2019). https://doi.org/10.1007/s10586-018-2244-6

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  • DOI: https://doi.org/10.1007/s10586-018-2244-6

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