JRM Vol.22 No.6 pp. 758-766
doi: 10.20965/jrm.2010.p0758


Auxiliary Particle Filter Localization for Intelligent Wheelchair Systems in Urban Environments

Masashi Yokozuka, Yusuke Suzuki, Toshinobu Takei,
Naohisa Hashimoto, and Osamu Matsumoto

Field Robotics Research Group, Intelligent Systems Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan

May 31, 2010
August 16, 2010
December 20, 2010
localization, data association problem, auxiliary particle filter, Monte Carlo localization, urban environment

We propose the robust 2D localization applies an Auxiliary Particle Filter (APF) to Monte Carlo Localization (MCL). Urban environments have fewer landmarks than two-dimensional (2D) indoor maps for efficiently finding a unique location. Localization using MCL have the problem that few landmarks pose divergence of the particles of MCL. We use APF for MCL, because APF continues resampling until convergence particle occurs in one localization step. Another problem with 2D urban mapping is that of data association posed by three-dimensional (3D) surfaces. Pitching and rolling may, for example, adversely affect 2D scan-data metrics due to 3D surfaces, causing mismatching data association in 2D maps. We therefore use a Laplacian filter for 2D grid maps. Experimental results show that our localization method is more highly stable in urban environments than MCL.

Cite this article as:
M. Yokozuka, Y. Suzuki, T. Takei, <. Hashimoto, and O. Matsumoto, “Auxiliary Particle Filter Localization for Intelligent Wheelchair Systems in Urban Environments,” J. Robot. Mechatron., Vol.22, No.6, pp. 758-766, 2010.
Data files:
  1. [1] Tohoku regional bureau ministry of land, “Implementation of Compact Cities,” report of Tohoku regional bureau ministry of land, 2003. (in Japanese)
  2. [2] O. Matsumoto et al., “Autonomous Traveling Control of the “TAO Aicle” Intelligent Wheelchair,” Proc. of. 2006 IEEE/RSJ Int. Conf. on. Intelligent Robots and Systems (IROS06), 2006.
  3. [3] F. Dellaert et al., “Monte Carlo Localization for Mobile Robots,” Proc. of. IEEE Int. Conf. Robot. Autom., 1999.
  4. [4] A. Howard et al., “Towards 3D Mapping in Large Urban Environments,” Proc. of. 2004 IEEE/RSJ Int. Conf. on. Intelligent Robots and Systems (IROS 04), 2004.
  5. [5] D. Borrmann et al., “Globally consistent 3D mapping with scan matching,” J. of Robotics and Autonomous Systems, Vol.56, No.2, pp. 130-142, 2008.
  6. [6] H. Surmann et al., “6D SLAM–Preliminary Report on Closing The Loop in Six Dimensions,” In Proc. of the 5th IFAC Symposium on Intelligent Autonomous Vehicles, 2004.
  7. [7] A. Nuchter et al., “Heuristic-Based Laser Scan Matching for Outdoor 6D SLAM,” In Advances in artificial intelligence. 28th annual German Conf. on AI, 2005, pp. 304-319, 2005.
  8. [8] M. k. Pitt et al., “Filtering via simulation: Auxiliary particle filter,” J. of American Statistical Assosiation, Vol.94, pp. 590-599, 1999.
  9. [9] M. Montemerlo et al., “FastSLAM: A factored solution to the simultaneous localization and mapping problem,” In Proc. of the AAAI National Conf. on Artificial Intelligence, Edmonton, Canada AAAI, 2002.
  10. [10] M. Montemerlo et al., “FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges,” Proc. of. Int. Conf. on Artificial Intelligence, pp. 1151-1156, 2003.
  11. [11] D. Hähnel et al., “An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements,” Proc. of. 2006 IEEE/RSJ Int. Conf. on. Intelligent Robots and Systems (IROS03), 2006.
  12. [12] G. Grisetti et al., “Improving grid-based slam with raoblackwellized particle filters by adaptive proposals and selective resampling,” Proc. of. IEEE Int. Conf. Robot. Autom., pp. 2443-2448, 2005.
  13. [13] C. Gao et al., “Towards Autonomous Wheelchair Systems in Urban Environments,” the 7th Int. Conf. on Field and Service Robotics, 2009.
  14. [14] F. Ramos et al., “Recognising and Modelling Landmarks to Close Loops in Outdoor SLAM,” Proc. of. IEEE Int. Conf. Robot. Autom., 2007.
  15. [15] J. Nieto et al, “Recursive scan-matching SLAM,” Robotics and Autonomous Systems, Vol.55, pp. 39-49, 2007.
  16. [16] D. Comaniciu et al., “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.24, No.5, pp. 604-619, 2002.
  17. [17] S. Thrun et al., “Probabilistic Robotics,” The MIT Press, Sep. 1, 2005.
  18. [18] S. Thrun, “A Probabilistic Online Mapping Algorithm for Teams of Mobile Robots,” Int. J. of Robotics Research, 2001.

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