Skip to main content
SpringerLink
Log in

Robot Tracking in SLAM with Masreliez-Martin Unscented Kalman Filter

  • Regular Paper
  • Control Theory and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

The simultaneous localization and mapping (SLAM) is a significant topic in intelligent robot. In this paper, a robot tracking algorithm in SLAM with Masreliez-Martin unscented Kalman filter (MMUKF) is proposed. A robot dynamic model based on SLAM characteristics is first used as state equation to model the robotic movement, and the measurement equations are deduced by linearizing the motion model. Next, the covariance of process noise is estimated with an adaptive factor to improve tracking performance in the MMUKF. Finally, the MMUKF is employed to estimate the positions of robot and landmarks. The proposed algorithm can complete robot tracking with good accuracy, and obtain reliable state estimation in SLAM. Simulation results reveal the validity of the proposed algorithm.

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

Similar content being viewed by others

References

  1. M. Mustafa, A. Stancu, N. Delanoue, and E. Codres, “Guaranteed SLAM-An interval approach,” Robotics and Autonomous Systems, vol. 100, pp. 160–170, February 2018.

    Article  Google Scholar 

  2. D. Amin and S. Govilkar, “Comparative study of augmented reality SDKs,” International Journal on Computational Science and Applications, vol. 5, no. 1, pp. 11–26, February 2015.

    Article  Google Scholar 

  3. H. Alazki, E. Herná ndez, J. M. Ibarra, and A. Poznyak, “Attractive ellipsoid method controller under noised measurements for SLAM,” International Journal of Control, Automation and Systems, vol. 15, no. 6, pp. 2764–2775, December 2017.

    Article  Google Scholar 

  4. L. Pan, J. Cheng, and Q. Zhang, “UFSM VO: Stereo odometry based on uniformly feature selection and strictly correspondence matching,” Proc. of 25th IEEE International Conference on Image Processing (ICIP), pp. 4148–4152, October 2018.

    Google Scholar 

  5. F. Mutz, L. P. Veronese, and T. Oliveira-Santos, “Largescale mapping in complex field scenarios using an autonomous car,” Expert Systems with Applications, vol. 46, pp. 439–462, March 2016.

    Article  Google Scholar 

  6. M. Abouzahir, A. Elouardi, R. Latif, S. Bouaziz, and A. Tajer, “Embedding SLAM algorithms: Has it come of age?,” Robotics and Autonomous Systems, vol. 100, pp. 14–26, February 2018.

    Article  Google Scholar 

  7. Y. He, C. Song, P. Yang, and X. Lei, “Bio-inspired guiding strategy for robot seeking intermittent information source,” 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 161–166, January 2016.

    Google Scholar 

  8. G. Piumatti, F. Lamberti, and A. Sanna, “Robust robot tracking for next-generation collaborative robotics-based gaming environments,” IEEE Trans. on Emerging Topics in Computing, November 2017.

    Google Scholar 

  9. J. Tang, B. Yu, S. Liu, Z. Zhang, W. Fang, and Y. Zhang, “PI-SoC: Heterogeneous SoC architecture for visual inertial SLAM applications,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8302–8307, October 2018.

    Google Scholar 

  10. J. Aulinas, Y. R. Petillot, J. Salvi, and X. Llado, “The SLAM problem: A survey,” CCIA, vol. 184, no. 1, pp. 363–371, 2008.

    Google Scholar 

  11. R. Smith, M. Self, and P. Cheeseman, “Estimating uncertain spatial relationships in robotics,” Machine Intelligence and Pattern Recognition, vol. 5, no. 5, pp. 435–461, 1988.

    MATH  Google Scholar 

  12. P. Moutarlier and R. Chatila, “An experimental system for incremental environment modelling by an autonomous mobile robot,” Proc. of International Symposium on Experimental Robotics I, vol. 139, pp. 327–346, 1989.

    Google Scholar 

  13. A. J. Davison, “Real-time simultaneous localisation and mapping with a single camera,” Proc. of IEEE International Conference on Computer Vision, pp. 1403–1410, October 2003.

    Chapter  Google Scholar 

  14. J. Andrade- Cetto, T. Vidal-Calleja, and A. Sanfeliu, “Unscented transformation of vehicle states in SLAM,” Proc. of IEEE International Conference on Robotics and Automation, pp. 323–328, April 2005.

    Google Scholar 

  15. R. Martinez-Cantin and J. A. Castellanos, “Unscented SLAM for large-scale outdoor environments,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3427–3432, August 2005.

    Google Scholar 

  16. H. K. Nguyen and M. Wongsaisuwan, “A study on unscented SLAM with path planning algorithm integration,” Proc. of 11th International Conference on Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 1–5, May 2014.

    Google Scholar 

  17. F. Zhang, X. Zhou, X. Chen, and R. Liu, “Particle filter for underwater bearings-only passive target tracking,” IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, vol. 2, pp. 847–851, December 2008.

    Google Scholar 

  18. R. van der Merwe and E. A. Wan, “The square-root unscented Kalman filter for state and parameter-estimation,” Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3461–3464, May 2001.

    Google Scholar 

  19. S. Holmes, G. Klein, and D. W. Murray, “A square root unscented Kalman filter for visual mono SLAM,” Proc. of IEEE International Conference on Robotics and Automation, Pasadena, pp. 3710–3716, May 2008.

    Google Scholar 

  20. L. Zhao, L. Ge, K. Wang, and R. Li, “A hybrid SLAM method for service robots in indoor environment,” Proc. of The 30th Chinese Control Conference, pp. 4034–4039, July 2011.

    Google Scholar 

  21. A. Al-Hussein and A. Haldar, “Unscented Kalman filter with unknown input and weighted global iteration for health assessment of large structural systems,” Structural Control and Health Monitoring, vol. 23, no.1, pp. 156–175, January 2016.

    Article  Google Scholar 

  22. A. Chatterjee and F. Matsuno, “A neuro-fuzzy assisted extended Kalman filter-based approach for simultaneous localization and mapping (SLAM) problems,” IEEE Trans. on Fuzzy Systems, vol. 15, no. 5, pp. 984–997, October 2007.

    Article  Google Scholar 

  23. T. Lee, C. Kim, and D. D. Cho, “A monocular vision sensor-based efficient SLAM method for indoor service robots,” IEEE Trans. on Industrial Electronics, vol. 66, no. 1, pp. 318–328, April 2018.

    Article  Google Scholar 

  24. B. Balasuriya, B. Chathuranga, B. Jayasundara, N. Napagoda, S. Kumarawadu, D. Chandima, and A. Jayasekara, “Outdoor robot navigation using gmapping based SLAM algorithm,” Proc. of Moratuwa Engineering Research Conference (MERCon), pp. 403–408, April 2016.

    Google Scholar 

  25. J. Kim and S. Sukkarieh, “Real-time implementation of airborne inertial-SLAM,” Robotics and Autonomous Systems, vol. 55, no. 1, pp. 62–71, January 2007.

    Article  Google Scholar 

  26. A. Palomer, P. Ridao, and D. Ribas, “Multibeam 3D underwater SLAM with probabilistic registration,” Sensors, vol. 16, no. 4, pp. 1–23, January 2016.

    Article  Google Scholar 

  27. P. Du, J. Han, J. Wang, G. Wang, D. Jing, X. Wang, and F. Qu, “View-based underwater SLAM using a stereo camera,” OCEANS 2017-Aberdeen, pp. 1–6, June 2017.

    Google Scholar 

  28. N. Ammann and L. Mayo, “Undelayed initialization of inverse depth parameterized landmarks in UKF-SLAM with error state formulation,” Proc. of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 918–923, July 2018.

    Google Scholar 

  29. T. Nemoto, K. Onodera, R. E. Mohan, M. Iwase, and K. Wood, “An application of the simultaneous localization and mapping (SLAM) method based on the unscented Kalman filter (UKF) to a reconfigurable quadruped robot with crawling locomotion,” Proc. of International Conference on Reconfigurable Mechanisms and Robots (ReMAR), pp. 1–8, June 2018.

    Google Scholar 

  30. J. H. Yoon, D. Y. Kim, and V. Shin, “Window length selection in linear receding horizon filtering,” Proc. of International Conference on Control, Automation and Systems, pp. 2463–2467, October 2008.

    Google Scholar 

  31. W. Li, S. Sun, Y. Jia, and J. Du, “Robust unscented Kalman filter with adaptation of process and measurement noise covariances,” Digital Signal Processing, vol. 48, pp. 93–103, January 2016.

    Article  MathSciNet  Google Scholar 

  32. Y. Wang, X. Lin, M. Zhu, and Z. Bai, “Robust estimation using the Huber function with a data-dependent tuning constant,” Journal of Computational and Graphical Statistics, vol. 16, no. 2, pp. 468–481, 2007.

    Article  MathSciNet  Google Scholar 

  33. C. Hajiyev and H. E. Soken, “Robust adaptive unscented Kalman filter for attitude estimation of pico satellites,” International Journal of Adaptive Control and Signal Processing, vol. 28, no. 2, pp. 107–120, February 2014.

    Article  MathSciNet  Google Scholar 

  34. “Matlab Utilities},” [Online]. Available: http://www.acfr.usyd.edu.au/homepages/academic/tbailey/software/software.html

Download references

Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology (DUT), No. 2 Linggong Road, Ganjingzi District, Dalian, 116024, China

    Ming Tang, Zhe Chen & Fuliang Yin

Authors
  1. Ming Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhe Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fuliang Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fuliang Yin.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Huanqing Wang under the direction of Editor Myo Taeg Lim. This work was supported by National Natural Science Foundation of China (Nos.61771091,61871066), National High Technology Research and Development Program (863 Program) of China (No.2015AA016306), Natural Science Foundation of Liaoning Province of China (No.20170540159), and Fundamental Research Funds for the Central Universities of China (No.DUT17LAB04).

Ming Tang received his B.S. degree in electronic information engineering, and an M.S. degrees in optics from Liaoning Normal University (LNNU), Dalian, China, in 2012 and 2016, respectively. He is currently working toward a Ph.D. degree in signal and information processing in the School of Information and Communication Engineering, Dalian University of Technology (DUT), Dalian, China. His research interests include image processing, robot localization, and tracking.

Zhe Chen received his B.S. degree in electronic engineering, an M.S. and a Ph.D. degrees in signal and information processing from Dalian University of Technology (DUT), Dalian, China, in 1996, 1999, and 2003, respectively. He joined the Department of Electronic Engineering, DUT, as a lecturer in 2002, became an associate professor in 2006, and has been a professor since 2017. His research interests include speech processing, image processing and wideband wireless communication.

Fuliang Yin was born in Fushun city, Liaoning province, China, in 1962. He received his B.S. degree in electronic engineering and an M.S. degree in communications and electronic systems from Dalian University of Technology (DUT), Dalian, China, in 1984 and 1987, respectively. He joined the Department of Electronic Engineering, DUT, as a Lecturer in 1987 and became an Associate Professor in 1991. He has been a Professor at DUT since 1994, and the Dean of the School of Electronic and Information Engineering of DUT from 2000 to 2009. His research interests include speech processing, image processing and broadband wireless communication.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, M., Chen, Z. & Yin, F. Robot Tracking in SLAM with Masreliez-Martin Unscented Kalman Filter. Int. J. Control Autom. Syst. 18, 2315–2325 (2020). https://doi.org/10.1007/s12555-019-0669-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-019-0669-1

Keywords

Search

Navigation