Skip to main content
SpringerLink
Log in

A Mobile Robot Localization Method Based on Polar Scan Matching and Adaptive Niching Chaos Optimization Algorithm

  • Short paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Mobile robot localization, which enables a robot to recognize its position and orientation in the environment, is one of the most principal issues in the field of autonomous navigation of mobile robots. Among the various methods to solve robot localization, polar scan matching (PSM) technique has the advantage of much less computational load but has the potential to mismatch scans. Chaos optimization algorithms (COAs), a family of stochastic global optimization algorithms based on chaos theory, has many good features such as easy implementation, short execution time and robust mechanism for escaping from local optima. This paper presents a mobile robot localization method based on polar scan matching and adaptive niching chaos optimization algorithm (ANCOA). First, we define a new error function that fits the characteristics of the polar scan matching problem, and then propose an adaptive version of niching chaos optimization algorithm to increase the search speed and improve solution accuracy. The task of robot localization is performed by optimizing the new error function using the adaptive version of NCOA. The proposed approach is tested and evaluated comprehensively through computer simulations which shows that the proposed approach can significantly improve the performance of polar scan matching.

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

Abbreviations

PSM:

Polar scan matching

ICP:

Iterative Closest Point

NDT:

Normal Distributions Transform

LRF:

Laser Range Finder

NCOA:

Niching Chaos Optimization Algorithm

ANCOA:

an Adaptive version of Niching Chaos Optimization Algorithm

SAI:

Scan Acquisition Interval

RMS:

Root Mean Square

STD:

Standard Deviation

TE:

Translation Error

RE:

Rotation Error

SR:

Success Rate

References

  1. Mirkhani, M., Forsati, R., Shahri, A.M., Moayedikia, A.: A novel efficient algorithm for mobile robot localization. Robot. Auton. Syst. 61, 920–931 (2013)

    Article  Google Scholar 

  2. Suzuki, T.: Mobile robot localization with GNSS multipath detection using pseudorange residuals. Adv. Robot. 33(12), 602–613 (2019)

    Article  Google Scholar 

  3. Kunhoth, J., Karkar, A., Al-Maadeed, S., Al-Ali, A.: Indoor positioning and wayfinding systems: a survey. Human-centric Computing and Information Sciences. 10, 18 (2020)

    Article  Google Scholar 

  4. Martinelli, F.: Simultaneous localization and mapping using the phase of passive UHF-RFID signals. J. Intell. Robot. Syst. 94(3–4), 711–725 (2019)

    Article  Google Scholar 

  5. Pau, G., Collotta, M., Maniscalco, V., Raymond, K.-K.C.: A fuzzy-PSO system for indoor localization based on visible light communications. Soft. Comput. 23(14), 5547–5557 (2019)

    Article  Google Scholar 

  6. Zhou, F.-Y., Yuan, X.-F., Yang, Y., Jiang, Z.-F., Zhou, C.-L.: A high precision visual localization sensor and its working methodology for an indoor mobile robot. Frontiers of Information Technology & Electronic Engineering. 17(4), 365–374 (2016)

    Article  Google Scholar 

  7. Bader, K., Lussier, B., Schön, W.: A fault tolerant architecture for data fusion: a real application of Kalman filters for mobile robot localization. Robot. Auton. Syst. 88, 11–23 (2017)

    Article  Google Scholar 

  8. Zhang, Q., Wang, P., Chen, Z.: An improved particle filter for mobile robot localization based on particle swarm optimization. Expert Syst. Appl. 135, 181–193 (2019)

    Article  Google Scholar 

  9. Tzafestas, S.G.: Mobile robot control and navigation: a global overview. J. Intell. Robot. Syst. 91(1), 35–58 (2018)

    Article  Google Scholar 

  10. Besl, P.J., McKay, N.D.: A method for registration of 3D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  11. Marani, R., Reno, V., Nitti, M., D’Orazio, T., Stella, E.: A modified iterative closest point algorithm for 3D point cloud registration. Computer-Aided Civil and Infrastructure Engineering. 31, 515–534 (2016)

    Article  Google Scholar 

  12. Biber, P., Strasser, W.: The normal distributions transform: a new approach to laser scan matching. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2743–2748 (2003)

  13. Ryu, K., Dantanarayana, L., Furukawa, T., Dissanayake, G.: Grid-based scan-to-map matching for accurate 2D map building. Adv. Robot. 30(7), 431–448 (2016)

    Article  Google Scholar 

  14. Diosi, A., Kleeman, L.: Fast laser scan matching using polar coordinates. Int. J. Robot. Res. 26(10), 1125–1153 (2007)

    Article  Google Scholar 

  15. Friedman, C., Chopra, I., Rand, O.: Perimeter-based polar scan matching (PB-PSM) for 2D laser Odometry. J. Intell. Robot. Syst. 80, 231–254 (2015)

    Article  Google Scholar 

  16. Ulas, C., Temeltas, H.: 3D multi-layered Normal distribution transform for fast and long range scan matching. J. Intell. Robot. Syst. 71, 85–108 (2013)

    Article  Google Scholar 

  17. Tao, L., Bui, T., Hasegawa, H.: Global ray-casting range image registration. IPSJ Transactions on Computer Vision and Applications. 9(14), 1–15 (2017)

    Google Scholar 

  18. Obadan, S., Wang, Z.-H.: A multi-objective optimization approach to robot localization of single and multiple emission sources. Procedia Manufacturing. 35, 755–761 (2019)

    Article  Google Scholar 

  19. Neto, W.A., Pinto, M.F., Marcato, A.L.M., SilvaJr, I.C., Fernandes, D.A.: Mobile robot localization based on the novel leader-based bat algorithm. J. Cont. Autom. Elect. Syst. 30(3), 337–346 (2019)

    Article  Google Scholar 

  20. Rim, C.-M., Piao, S.-H., Li, G., Pak, U.-S.: A niching chaos optimization algorithm for multimodal optimization. Soft. Comput. 22, 621–633 (2018)

    Article  Google Scholar 

  21. Rim, C.-M., Rim, C.-H., Chen, G., Sin, Y.-C., Kim, K.-C.: A kinematic calibration method of the articulated arm coordinate measuring machine using niching Chaos optimization algorithm. J. Test. Eval. https://doi.org/10.1520/JTE20180174

  22. Weisstein, E. W.: Circle map. MathWorld–A Wolfram Web Resource. [Online]. Available: http://mathworld.wolfram.com/CircleMap.html

  23. Mahfoud, S. W.: Crowding and Preselection Revisited. In: 2nd conference on Parallel Problem Solving from Nature (PPSN–92), Brussels, Belgium, 27–36 (1992)

  24. Yang, J., Li, H., Jia, Y.: Go-ICP: Solving 3D registration efficiently and globally optimally. In: 2013 IEEE International Conference on Computer Vision, Sydney. 1457–1464 (2013)

  25. Goldberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Massachusetts: Addison–Wesley, 1989

  26. Wolfgang, H., Kohler, D., Rapp, H., Andor, D.: Real-time loop closure in 2D LIDAR SLAM. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016

Download references

Acknowledgements

The authors thank the referee for careful reading and helpful suggestions on the improvement of the manuscript.

Code or Data Availability

Not applicable.

Author information

Authors and Affiliations

  1. Department of Electronics & Automation, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea

    Chol-Min Rim, Yong-Chol Sin & Kang-Hyok Paek

Authors
  1. Chol-Min Rim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong-Chol Sin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kang-Hyok Paek
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mr. Rim proposed an adaptive version of the niching chaos optimization algorithm and wrote the manuscript. Prof. Sin proposed a new error function suitable for scan matching problems. Mr. Paek was responsible for computer simulation.

Corresponding author

Correspondence to Chol-Min Rim.

Ethics declarations

Conflicts of Interest/Competing Interests

The authors declare that they have no conflict of interest.

Ethics Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent to Participate

All authors carried out the proof and conceived of the study.

Consent for Publication

All the authors read the last manuscript and agreed to contribute to the journal.

Additional information

Publisher’s Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rim, CM., Sin, YC. & Paek, KH. A Mobile Robot Localization Method Based on Polar Scan Matching and Adaptive Niching Chaos Optimization Algorithm. J Intell Robot Syst 106, 19 (2022). https://doi.org/10.1007/s10846-022-01724-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-022-01724-y

Keywords

Search

Navigation