A Density-Assisted Particle Filter for Mobile Robot Localization with Uncertain Environment MAP

We present in this paper a modified density-assisted particle filter for indoor mobile robot localization in a situation where the environment map is subject to random uncertainties and is not perfectly known to the tracker. The proposed filter jointly estimates the robot's pose and the environ...

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Bibliographic Details
Published in2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07 Vol. 3; pp. III-1205 - III-1208
Main Authors Silva, P. R. A., Bruno, M. G. S.
Format Conference Proceeding
LanguageEnglish
Welsh
Published IEEE 01.04.2007
Subjects
Data mining
Mobile robots
Monte Carlo methods
Nonlinear estimation
Parametric statistics
Particle filters
Particle tracking
Sliding mode control
State estimation
State-space methods
Testing
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ISBN9781424407279
1424407273
ISSN1520-6149
DOI10.1109/ICASSP.2007.367059

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Summary:We present in this paper a modified density-assisted particle filter for indoor mobile robot localization in a situation where the environment map is subject to random uncertainties and is not perfectly known to the tracker. The proposed filter jointly estimates the robot's pose and the environment map parameters combining raw measurements from a range-finding laser scanner and the robot's odometric data. Experiments with real data show promising results even in adverse scenarios with abrupt maneuvers and heavily cluttered environments.
ISBN:9781424407279
1424407273
ISSN:1520-6149
DOI:10.1109/ICASSP.2007.367059