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Enhanced Monte Carlo localization incorporating a mechanism for preventing premature convergence

Published online by Cambridge University Press:  20 May 2016

Chiang-Heng Chien ,
Wei-Yen Wang ,
Jun Jo  and
Chen-Chien Hsu
Chiang-Heng Chien
Affiliation:
Department of Electrical Engineering, National Taiwan Normal University, 162 He-Ping East Rd., Sec. 1, Taipei 10610, Taiwan. E-mails: [email protected], [email protected]
Wei-Yen Wang
Affiliation:
Department of Electrical Engineering, National Taiwan Normal University, 162 He-Ping East Rd., Sec. 1, Taipei 10610, Taiwan. E-mails: [email protected], [email protected]
Jun Jo
Affiliation:
School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD 4222, Australia. E-mail: [email protected]
Chen-Chien Hsu*
Affiliation:
Department of Electrical Engineering, National Taiwan Normal University, 162 He-Ping East Rd., Sec. 1, Taipei 10610, Taiwan. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

In this paper, we propose an enhanced Monte Carlo localization (EMCL) algorithm for mobile robots, which deals with the premature convergence problem in global localization as well as the estimation error existing in pose tracking. By incorporating a mechanism for preventing premature convergence (MPPC), which uses a “reference relative vector” to modify the weight of each sample, exploration of a highly symmetrical environment can be improved. As a consequence, the proposed method has the ability to converge particles toward the global optimum, resulting in successful global localization. Furthermore, by applying the unscented Kalman Filter (UKF) to the prediction state and the previous state of particles in Monte Carlo Localization (MCL), an EMCL can be established for pose tracking, where the prediction state is modified by the Kalman gain derived from the modified prior error covariance. Hence, a better approximation that reduces the discrepancy between the state of the robot and the estimation can be obtained. Simulations and practical experiments confirmed that the proposed approach can improve the localization performance in both global localization and pose tracking.

Keywords

Robot localizationMonte Carlo localizationPremature convergenceMobile robotUnscented Kalman filterNavigation
Type
Articles
Information
Robotica , Volume 35 , Issue 7 , July 2017 , pp. 1504 - 1522
Copyright
Copyright © Cambridge University Press 2016 

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References

1. Leonard, J.-J. and Durrant-Whyte, H.-F., “Mobile robot localization by tracking geometric beacons,” IEEE Trans. Robot. Autom. 7 (3), 376–382 (1991).Google Scholar
2. Burgard, W., Fox, D., Henning, D. and Schmidt, T., “Estimating the Absolute Position of a Mobile Robot Using Position Probability grids,” Proceedings of the National Conference on Artificial Intelligence, Oregon (Aug. 1996) pp. 896–901.Google Scholar
3. Cassandra, A.-R., Kaelbling, L.-P. and Kurien, J.-A., “Acting Under uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 2, (1996) pp. 287–295.Google Scholar
4. Dellaert, F., Fox, D., Burgard, W. and Thrun, S., “Monte Carlo Localization for mobile robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, USA (May 1999) pp. 1322–1328.Google Scholar
5. Jensfelt, P. and Kristensen, S., “Active global localization for a mobile robot using multiple hypothesis tracking,” IEEE Trans. Robot. Autom. 17 (5), 748–760 (2001).CrossRefGoogle Scholar
6. Andrieu, C., Doucet, A. and Holenstein, R., “Particle Markov chain Monte Carlo methods,” J. R. Stat. Soc. 72 (3), 269–342 (2010).CrossRefGoogle Scholar
7. Thrun, S., Fox, D., Burgard, W. and Dellaert, F., “Robust Monte Carlo localization for mobile robots,” Int. J. Artif. Intell., 128 (1–2), 99–141 (2001).CrossRefGoogle Scholar
8. Milstein, A., Sanchez, J.-N. and Williamson, E.-T., “Robust Global Localization Using Clustered Particle Filtering,” Proceedings of the National Conference on American Association for Artificial Intelligence, Alberta (Jul. 2002) pp. 581–586.Google Scholar
9. Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics, 3rd ed. (The MIT Press, England, 2005).Google Scholar
10. Kuo, C.-J., Hsu, C.-C. and Kao, W.-C., “Improved Monte Carlo Localization with Robust Estimation for Mobile Robots,” Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Manchester (Oct. 2013) pp. 3651–3656.Google Scholar
11. Bienvenue, A., Joannides, M., Berard, J., Fontenas, E. and Francois, O., “Niching in Monte Carlo Filtering Algorithms,” Proceedings of the International Conference on Artificial Evolution, Le Creusot (Oct. 2001) pp. 19–30.Google Scholar
12. Menegatti, E., Pretto, A., Scarpa, A. and Pagello, E., “Omnidirectional vision scan matching for robot localization in dynamic environments,” IEEE Trans. Robot. 22 (3), 523–535 (2006).Google Scholar
13. Hsu, C.-C., Wong, C.-C., Teng, H.-C. and Ho, C.-Y., “Localization of mobile robots via an enhanced particle filter incorporating tournament selection and nelder-mead simplex search,” Int. J. Innovative Comput., Inf. Control 7 (7A), 3725–3737 (2011).Google Scholar
14. Li, T., Sun, S. and Duan, J., “Monte Carlo Localization for Mobile Robot Using Adaptive Particle Merging and Splitting Technique,” Proceedings of the IEEE International Conference on Information and Automation, Harbin (Jun. 2010) pp. 1913–1918.CrossRefGoogle Scholar
15. Li, T., Sun, S., Sattar, T.-P. and Corchado, J.-M., “Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches,” Int. J. Expert Syst. Appl. 41 (8), 3944–3954 (2014).CrossRefGoogle Scholar
16. Sarkar, B., Saha, S. and Pal, P.-K., “A novel method for computation of importance weights in Monte Carlo localization on line segment-based maps,” Robot. Auton. Syst. 74, 51–65 (2015).Google Scholar
17. Ronghua, L. and Bingrong, H., “Coevolution based adaptive Monte Carlo localization (CEAMCL),” Int. J. Adv. Robot. Syst. 1 (3), 183–190 (2004).CrossRefGoogle Scholar
18. Menczer, F., Degeratu, M. and Street, W.-N., “Efficient and scalable pareto optimization by evolutionary local selection algorithms,” J. Evolutionary Comput. 8 (2), 223–247 (2000).Google Scholar
19. Kootstra, G. and Boer, B.-D., “Tackling the premature convergence problem in Monte Carlo localization,” Robot. Auton. Syst. 57 (11), 1107–1118 (2009).CrossRefGoogle Scholar
20. Zhang, L., Zapata, R. and Lépinay, P., “Self-adaptive Monte Carlo Localization for mobile robots using range finders,” Robotica 30 (2), 229–244 (2012).Google Scholar
21. Maffei, R., Jorge, V.-A.-M., Rey, V.-F., Kolberg, M. and Prestes, E., “Fast Monte Carlo Localization Using Spatial Density Information,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle (May, 2015) pp. 6352–6358.Google Scholar
22. Kar, A., “Linear-time robot localization and pose tracking using matching signatures,” Robot. Auton. Syst. 60 (2), 296–308 (2012).CrossRefGoogle Scholar
23. He, T. and Hirose, S., “A global localization approach based on line-segment relation matching technique,” Robot. Auton. Syst. 60 (1), 95–112 (2012).Google Scholar
24. Choi, H., Kim, E., Park, Y.-W. and Kim, C.-H., “Multiple Hypothesis Tracking for Mobile Robot Localization,” Proceedings of the IEEE International SICE Annual Conference, Akita (Aug., 2012) pp. 1574–1578.Google Scholar
25. Julier, S.-J. and Uhlmann, J.-K., “A New Extension of the Kalman Filter to Nonlinear Systems,” Proceedings of the International Symposium on Aerospace/Defense Sensing, Simulations, and Control, Vol. 182, Florida, USA, (1997), pp. 182–193.Google Scholar
26. Rubin, D., Using the SIR Algorithm to Simulate Posterior Distributions, 1st ed. (Oxford University Press, USA, 1988).Google Scholar
27. Michalewicz, Z., Genetic Algorithm + Data Structure = Evolution Programs, 3rd ed. (Springer, USA, 1996).Google Scholar
28. Doane, D.-P., “Aesthetic frequency classification,” J. Am. Stat. 30 (4), 181–183 (1976).Google Scholar
29. Kandepu, R., Foss, B. and Imsland, L., “Applying the unscented Kalman filter for nonlinear state estimation,” Int. J. Process Control 18 (7–8), 753–768 (2008).CrossRefGoogle Scholar
30. Kalman, R.-E., “Contibutions to the theory of optimal control,” National J. Boletin de la Seciedad Matematica Mexicana 5, 102–119 (1960).Google Scholar
31. Kalman, R.-E., “A new approach to linear filtering and prediction problems,” J. Basic Eng. 82, 35–45 (1960).Google Scholar
32. Kalman, R.-E. and Brucy, R. S., “New results in linear filtering and prediction theory,” J. Basic Eng. 83, 95–108 (1960).Google Scholar
33. der Merwe, R.-V., Sigma-Point Kalman Filters for Probability Inference in Dynamic State-Space Models, Ph.D Thesis (USA, Oregon Health and Science University, 2004).Google Scholar
34. Lisowski, M., Fan, Z. and Ravn, O., “Differential Evolution to Enhance Localization of Mobile Robots,” Proceedings of the IEEE International Conference on Fuzzy Systems, Taipei (June, 2011) pp. 241–247.CrossRefGoogle Scholar
35. Schon, T.-B., Tornqvist, D. and Gusrafsson, F., “Fast Particle Filters for Multi-Rate Sensors,” Proceedings of the European Signal Processing Conference, Poznan (Sep., 2007) pp. 876–880.Google Scholar