Skip to main content
SpringerLink
Log in

On the Efficiency of SLAM Using Adaptive Unscented Kalman Filter

  • Research Paper
  • Published:
Iranian Journal of Science and Technology, Transactions of Mechanical Engineering Aims and scope Submit manuscript

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 via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

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

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

    Article  Google Scholar 

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

    Google Scholar 

  • 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

  • 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 

  • 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 

  • 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

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

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • 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 

  • 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

  • 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

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

    Article  Google Scholar 

  • 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 

  • 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

  • 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 

  • 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

  • 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 

  • 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

  • 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

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

    Book  Google Scholar 

  • 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

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

    Article  MathSciNet  Google Scholar 

  • 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

  • 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

  • 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 

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

    Google Scholar 

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

  • 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

Authors and Affiliations

  1. Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran

    Masoud Sotoodeh Bahraini

Authors
  1. Masoud Sotoodeh Bahraini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masoud Sotoodeh Bahraini.

Rights and permissions

Reprints and permissions

About this article

Check for updates. 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

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40997-019-00294-z

Keywords

Search

Navigation