Adaptive dynamic clustered particle filtering for mobile robots global localization

Journal of Intelligent and Robotic Systems: Theory and Applications

Volumn 53, Issue 1, 2008, Pages 57-85

  Liu, Zhibin ^a   Shi, Zongying ^a   Zhao, Mingguo ^a   Xu, Wenli ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Adaptive dynamic clustered particle filtering; Global localization; Mobile robots; Multimodal distribution; Multiple hypotheses

Indexed keywords

ACOUSTIC SIGNAL PROCESSING; CONTROL THEORY; ESTIMATION; MOBILE ROBOTS; NONLINEAR FILTERING; OBJECT RECOGNITION; ROBOTICS; ROBOTS; SIGNAL FILTERING AND PREDICTION;

ADAPTIVE DYNAMIC CLUSTERED PARTICLE FILTERING; CENTRAL LIMIT THEOREM; ESTIMATION ACCURACIES; ESTIMATION ERRORS; GLOBAL LOCALIZATION; IMPORTANCE SAMPLING; LOWER BOUNDS; MULTI-DIMENSIONAL SPACES; MULTI-MODAL; MULTIMODAL DISTRIBUTION; MULTIMODAL DISTRIBUTIONS; MULTIPLE HYPOTHESES; OVERLAPPING CLUSTERS; PARTICLE FILTERING; POSTERIOR DISTRIBUTIONS; ROBUST TRACKING; SAMPLE SIZES; STATISTIC THEORY; SUB-CLUSTERS;

CHLORINE COMPOUNDS;

EID: 51849134033     PISSN: 09210296     EISSN: 15730409     Source Type: Journal    
DOI: 10.1007/s10846-008-9229-2     Document Type: Article

References (26)

1. Jensfelt, P., Kristensen, S.: Active global localisation for a mobile robot using multiple hypothesis tracking. IEEE Trans. Robot. Autom. 17, (5), 748-760 (2001)
2. Arras, K., Castellanos, J.A., Siegwart, R.: Feature-based multi-hypothesis localization and tracking for mobile robots using geometric constraints. Proc. IEEE ICRA 2, 1371-1377 (2002)
3. Austin, D.J., Jensfelt, P.: Using multiple Gaussian hypotheses to represent probability distributions for mobile robot localization. Proc. IEEE ICRA 2, 1036-1041 (2000)
4. Burgard, W., Fox, D., Henning, D., Schmidt, T.: Estimating the absolute position of a mobile robot using position probability grids. In: Proceedings of the National Conference on Artificial Intelligence (AAAI-96), Portland, USA, pp. 896-901 (1996)
5. Fox, D., Burgard, W., Thrun, S.: Markov localization for mobile robots in dynamic environments. J. Artif. Intell. Res. 11, 391-427 (1999)
6. Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte Carlo localization for mobile robots. Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1322-1328. IEEE, Michigan (1999)
7. Jensfelt, P., Wijk, O., Austin, D.J., Andersson, M.: Experiments on augmenting condensation for mobile robot localization. Proc. IEEE ICRA 3, 2518-2524 (2000)
8. Fox, D., Burgard, W., Dellaert, F.: Monte Carlo localization: efficient position estimation for mobile robots. In: Proceedings of the 16th National Conf on Artificial Intelligence (AAAI-99), pp. 343-349. Robotics Institute, Pittsburgh (1999)
9. Thrun, S., Fox, D., Burgard, W. et al.: Robust Monte Carlo localization for mobile robots. Artif. Intell. 128, (1), 99-141 (2001)
10. George, D., Barnes, N.: Particle attraction localisation. Proc. Int. Conf. Intel. Robot Syst 2, 1240-1245 (2003)
11. Zhang, B., Tian, W., Jin, Z.: Head tracking based on the integration of two different particle filters. Meas. Sci. Technol. 17, (11), 2877-2883 (2006)
12. Fox, D.: KLD-sampling: Adaptive particle filters. Adv. Neural. Inform. Proces. Syst. 14, 713-720 (2002)
13. Fox, D.: Adapting the sample size in particle filters through KLD-sampling. Int. J. Robot. Res. 22, (12), 985-1003 (2003)
14. Soto, A.: Self adaptive particle filter. In: Proceedings of International Joint Conferences on Artificial Intelligence (IJCAI-05), Edinburgh, Scotland, pp. 1398-1406 (2005)
15. Grisetti, G., Stachniss, C., Burgard, W.: Improving grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling. Proc. IEEE ICRA. pp. 2443-2448 (2005)
16. Stachniss, C., Grisetti, G., Burgard, W.: Recovering particle diversity in a Rao-Blackwellized particle filter for SLAM after actively closing loops. Proc. IEEE ICRA. pp. 667-672 (2005)
17. Milstein, A., Sanchez, J.N., Williamson, E.T.: Robust global localization using clustered particle filtering. In: Proceedings of 18th National Conference on Artificial Intelligence AAAI-02, Edmonton, pp. 581-586 (2002)
18. Geweke, J.: Bayesian inference in econometric models using Monte Carlo Integration. Econometrica. 57, (6), 1317-1339 (1989)
19. Liu, J.S., Chen, R.: Blind deconvolution via sequential imputations. J. Am. Stat. Assoc. 90, (430), 567-576 (1995)
20. Doucet, A., et al.: On sequential Monte Carlo sampling methods for Bayesian filtering. Stat. Comput. 10, (3), 197-208 (2000)
21. Yang, T., Aitken, V.: Uniform clustered particle filtering for robot localization. Proc. Am. Control Conf 7, 4607-4612 (2005)
22. Roumeliotis, S.I., Bekey, G.A.: Distributed multirobot localization. IEEE Trans. Robot. Autom. 18, (5), 781-794 (2002)
23. Schmitt, T., Hanek, R., Beetz, M., Buck, S., Radig, B.: Cooperative probabilistic state estimation for vision-based autonomous mobile robots. IEEE Trans. Robot. Autom. 18, (5), 670-684 (2002)
24. Menegatti, E., Pretto, A., Scarpa, A., Pagello, E.: Omnidirectional vision scan matching for robot localization in dynamic environments. IEEE Trans. Robot. 22, (3), 523-535 (2006)
25. Röfer, T., Laue, T., Burkhard, H., Stryk, O., Schwiegelshohn, U. et al.: GermanTeam RoboCup 2005, Technical report (2005). Available online: http://www.germanteam.org/GT2005.pdf
26. Hoffmann, J., Spranger, M., Göhring, D., Jüngel, M.: Negative information and proprioception in Monte Carlo self-localization for a 4-legged robots. In: Nineteenth International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Agents in Real-Time and Dynamic Environments, Edinburgh, Scotland (2005)