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http://dx.doi.org/10.5302/J.ICROS.2009.15.6.619

An Improved Resampling Technique using Particle Density Information in FastSLAM  

Woo, Jong-Suk (서울대학교 전기컴퓨터공학부)
Choi, Myoung-Hwan (강원대학교 전기전자공학부)
Lee, Beom-Hee (서울대학교 전기컴퓨터공학부)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.15, no.6, 2009 , pp. 619-625 More about this Journal
Abstract
FastSLAM which uses the Rao-Blackwellized particle filter is one of the famous solutions to SLAM (Simultaneous Localization and Mapping) problem that estimates concurrently a robot's pose and surrounding environment. However, the particle depletion problem arises from the loss of the particle diversity in the resampling process of FastSLAM. Then, the performance of FastSLAM degenerates over the time. In this work, DIR (Density Information-based Resampling) technique is proposed to solve the particle depletion problem. First, the cluster is constructed based on the density of each particle, and the density of each cluster is computed. After that, the number of particles to be reserved in each cluster is determined using a linear method based on the distance between the highest density cluster and each cluster. Finally, the resampling process is performed by rejecting the particles which are not selected to be reserved in each cluster. The performance of the DIR proposed to solve the particle depletion problem in FastSLAM was verified in computer simulations, which significantly reduced both the RMS position error and the feature error.
Keywords
FastSLAM; particle filter; particle resampling; particle density; clustering;
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  • Reference
1 E. Schikuta, 'Grid clustering: An efficient hierarchical clustering method for very large data sets,' Proc. 13th Int. Conf. on Pattern Recognition, vol. 2, pp. 101-105, 1996   DOI
2 K. Alsabti, S. Ranka, and V. Singh, 'An efficient K-means clustering algorithm,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, issue. 7, pp. 881-892,2002   DOI   ScienceOn
3 N. Kwak, G W. Kim, and B. H. Lee, 'A new compensation technique based on analysis of resampling process in FastSLAM,' Robotica, vol. 26, no. 2, pp. 205-217, Mar. 2008   ScienceOn
4 S. Lee and S. Lee, 'Recursive particle filter with geometry constraints for SLAM,' IEEE. Int. Conf. Multisensor Fusion and Integration for Intelligent Systems, Heidelberg, pp. 395-401, 2006
5 S. Thrun, D. Fox, and W. Burgard, 'Monte carlo localization with mixture proposal distribution,' American Association for Artificial Intelligence, pp. 859-865, 2000
6 G Grisetti, G D. Tipaldi, and C. Stachniss, et al., 'Fast and accurate SLAM with rao-blackwellized particle filters,' Robotics andAutonomous Systems, vol. 55, pp. 30-38, Jan 2007   DOI   ScienceOn
7 M. Montemerlo and S. Thrun, 'imultaneous localization and mapping with unknown data association using fastslam,' Proceedings of the 2003 IEEE International Conforence on Robotics and Automation, pp. 185-1991   DOI
8 J. Kennedy and R. C. Eberhart, 'Particle swarm optimization,' Proceedings of IEEE International 1995 Conforence on Neural Networks, voI.4,pp. 1942-1948, 1995   DOI
9 M. Montemerlo, 'FastSLAM: A factored solution to the simultaneous localization and mapping problem with unknown data association,' Ph.D. thesis, Camegie Mellon University, 2003
10 M. Montemerlo and S. Thrun, 'Simultaneous localimtion and mapping with unknown data association using FastSLAM,' IEEE International Corrference on Robotics and Automation, pp. 1985-1991,2003
11 N. Kwak, I. K. Kim, and H. C. Lee, et al., 'Adaptive prior boosting technique for the efficient sample size in FastSLAM,' IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007
12 T. Bailey, J. Nieto, and E. Nebot, 'Consistency of the FastSLAM algorithm,' IEEE International Conference on Robotics and Automation, pp. 424-429, 2006
13 S. Thrun, W. Burgard, and D. Fox, 'Probabilistic robotics,' MIT Press, Cambridge, 2005
14 N. J. Gordon, D. J. Salmond, and A. F. M. Smith, 'Novel approach to nonlinear/non-gaussian Bayesian state estimation,' IEE-Proceedings- F. vol. 140, no. 2. pp. 107-113   DOI   ScienceOn
15 I. K. Kim, N. Kwak, H. C. Lee, and B. H. Lee, 'Improved particle filter using geometric relation between particles in FastSLAM,' Robotica, Oct. 2008. (accepted)
16 Y. Zhao and J. Song, 'GDILC:A grid-based density-isoline clustering algorithm,' Proceedings of International Conforence on Info-tech and lifo-net, 2001
17 M. Ester, H. Kriegel, J. Sander, and X. Xu, 'A density-based algorithm for discovering clusters in large spatial databases with noise,' inProc. KDD, pp. 226-231,1996