On the Efficiency of SLAM Using Adaptive Unscented Kalman Filter

Abstract

In this paper, an adaptive unscented Kalman filter (AUKF) algorithm is applied to simultaneous localization and mapping (SLAM), based on adaptation of a scaling parameter. The scaling parameter is a design parameter in the unscented Kalman filter (UKF) which can improve the quality of the approximation. An adaptive method is designed to find the suitable value for the scaling parameter to improve the accuracy of estimation. It is demonstrated that the proposed methodology significantly reduces the state estimation error and improves the navigation accuracy of an autonomous vehicle. Also, it is highlighted that the computational cost is not much affected by increasing the number of observations, especially in the SLAM application in which the number of landmarks is growing through estimation. A comparison between UKF and AUKF algorithms is also provided for the SLAM application. The efficiency and the robustness of the proposed algorithm are investigated by applying noise of different orders in simulation results. In addition, non-credibility indices are used to compare the relative performance of AUKF and UKF. The results illustrate that AUKF-SLAM is more accurate than UKF-SLAM.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. Bahraini MS, Bozorg M, Rad AB (2018a) A new adaptive UKF algorithm to improve the accuracy of SLAM. Int J Robot (To appear)

  2. Bahraini MS, Bozorg M, Rad AB (2018b) SLAM in dynamic environments via ML-RANSAC. Mechatronics 49:105–118

    Article  Google Scholar 

  3. Bailey T (2002) Mobile robot localisation and mapping in extensive outdoor environments. The University of Sydney, Diss

    Google Scholar 

  4. Bailey T, Nieto J, Guivant J, Stevens M, Nebot E (2006) Consistency of the EKF-SLAM algorithm. In: IEEE/RSJ international conference on intelligent robots and systems, 2006, IEEE, pp 3562–3568

  5. Cadena C et al (2016) Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans Robot 32:1309–1332

    Article  Google Scholar 

  6. Cugliari M, Martinelli F (2008) A FastSLAM algorithm based on the unscented filtering with adaptive selective resampling. In: Laugier C, Siegwart R (eds) Field and service robotics. Springer tracts in advanced robotics, vol 42. Springer, Berlin, Heidelberg

    Google Scholar 

  7. Dunik J, Straka O, Simandl M (2011) The development of a randomised unscented Kalman filter. In: Proceedings of the 18th IFAC world congress, Milan, Italy, pp 8–13

  8. Dunik J, Simandl M, Straka O (2012) Unscented Kalman filter: aspects and adaptive setting of scaling parameter. IEEE Trans Autom Control 57:2411–2416

    MathSciNet  Article  Google Scholar 

  9. Dunik J, Straka O, Simandl M (2013) Stochastic integration filter. IEEE Trans Autom Control 58:1561–1566

    MathSciNet  Article  Google Scholar 

  10. Guivant JE, Nebot EM (2001) Optimization of the simultaneous localization and map-building algorithm for real-time implementation. IEEE Trans Robot Autom 17:242–257

    Article  Google Scholar 

  11. Ho TS, Fai YC, Ming ESL (2015) Simultaneous localization and mapping survey based on filtering techniques. In: Control conference (ASCC), 2015 10th Asian, IEEE, pp 1–6

  12. Huang GP, Mourikis A, Roumeliotis S (2009) On the complexity and consistency of UKF-based SLAM. In: IEEE international conference on robotics and automation, ICRA’09, 2009, IEEE, pp 4401–4408

  13. Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear estimation. Proc IEEE 92:401–422

    Article  Google Scholar 

  14. Maeyama S, Takahashi Y, Watanabe K (2015) A solution to SLAM problems by simultaneous estimation of kinematic parameters including sensor mounting offset with an augmented UKF. Adv Robot 29:1137–1149

    Article  Google Scholar 

  15. Martinez-Cantin R, Castellanos J (2005) Unscented SLAM for large-scale outdoor environments. In: 2005 IEEE/RSJ international conference on intelligent robots and systems (IROS 2005), IEEE, pp 3427–3432

  16. Mohanty PK, Parhi DR (2014) Navigation of autonomous mobile robot using adaptive network based fuzzy inference system. J Mech Sci Technol 28:2861–2868

    Article  Google Scholar 

  17. Montemerlo M, Thrun S, Koller D, Wegbreit B (2002) FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: AAAI/IAAI, pp 593–598

  18. Qi J, Song D, Wu C, Han J, Wang T (2012) KF-based adaptive UKF algorithm and its application for rotorcraft UAV actuator failure estimation. Int J Adv Robot Syst 9:132

    Article  Google Scholar 

  19. Scardua LA, da Cruz JJ (2015) Adaptively tuning the scaling parameter of the unscented Kalman filter. In: CONTROLO’2014–Proceedings of the 11th Portuguese conference on automatic control, Springer, pp 429–438

  20. Shao G, Wan L, Shen XD (2013) Hierarchical map building based UKF-SLAM approach for AUV. In: Applied mechanics and materials, vol 437. Trans Tech Publications, pp 793–797

  21. Simon D (2006) Optimal state estimation: Kalman, H infinity, and nonlinear approaches. Wiley, Hoboken

    Google Scholar 

  22. Straka O, Duník J, Šimandl M (2011) Performance evaluation of local state estimation methods in bearings-only tracking problems. In: 2011 Proceedings of the 14th international conference on information fusion (FUSION), IEEE, pp 1–8

  23. Straka O, Dunik J, Simandl M (2014a) Unscented Kalman filter with advanced adaptation of scaling parameter. Automatica 50:2657–2664

    MathSciNet  Article  Google Scholar 

  24. Straka O, Dunik J, Simandl M, Blasch E (2014b) Comparison of adaptive and randomized unscented Kalman filter algorithms. In: 2014 17th international conference on information fusion (FUSION), IEEE, pp 1–8

  25. Sunderhauf N, Lange S, Protzel P (2007) Using the unscented Kalman filter in mono-SLAM with inverse depth parametrization for autonomous airship control. In: IEEE international workshop on safety, security and rescue robotics, SSRR 2007, IEEE, pp 1–6

  26. Wang H, Fu G, Li J, Yan Z, Bian X (2013) An adaptive UKF based SLAM method for unmanned underwater vehicle. Math Probl Eng 2013:1–12

    MathSciNet  MATH  Google Scholar 

  27. Wang Z-l, Qin S, Y-m Liang (2014) Adaptive UKF-SLAM algorithm based on noise scaling. Comput Eng 10:029

    Google Scholar 

  28. Wu M, Weng Y (2014) UKF-SLAM based gravity gradient aided navigation. In: Intelligent Robotics and Applications. Springer, pp 77-88

  29. Wu M, Yao J (2015) Adaptive UKF-SLAM based on magnetic gradient inversion method for underwater navigation. In: Liu H, Kubota N, Zhu X, Dillmann R, Zhou D (eds) Intelligent robotics and applications. ICIRA 2015. Lecture Notes in Computer Science, vol 9245. Springer, Cham

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Masoud Sotoodeh Bahraini.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bahraini, M.S. On the Efficiency of SLAM Using Adaptive Unscented Kalman Filter. Iran J Sci Technol Trans Mech Eng 44, 727–735 (2020). https://doi.org/10.1007/s40997-019-00294-z

Download citation

Keywords

  • SLAM
  • Unscented Kalman filter
  • Mobile robots
  • Robot navigation
  • Adaptive UKF