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

Paper:

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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

Received:
May 31, 2010
Accepted:
August 16, 2010
Published:
December 20, 2010
Creative Commons License
Keywords:
localization, data association problem, auxiliary particle filter, Monte Carlo localization, urban environment
Abstract
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, N. 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.
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