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Particle filtering on the Euclidean group: framework and applications

Published online by Cambridge University Press:  01 November 2007

Junghyun Kwon ,
Minseok Choi ,
F. C. Park  and
Changmook Chun
Junghyun Kwon
Affiliation:
School of Mechanical and Aerospace Engineering, Seoul National University, Seoul 151-742, Korea
Minseok Choi
Affiliation:
School of Mechanical and Aerospace Engineering, Seoul National University, Seoul 151-742, Korea
F. C. Park*
Affiliation:
School of Mechanical and Aerospace Engineering, Seoul National University, Seoul 151-742, Korea
Changmook Chun
Affiliation:
Intelligent Robotics Research Center, Korea Institute of Science and Technology, Seoul 136-791, Korea
*
*Corresponding author. E-mail: fcp@snu.ac.kr
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Summary

We address general filtering problems on the Euclidean group SE(3). We first generalize, to stochastic nonlinear systems evolving on SE(3), the particle filter of Liu and West for simultaneous estimation of the state and covariance. The filter is constructed in a coordinate-invariant way, and explicitly takes into account the geometry of SE(3) and P(n), the space of symmetric positive definite matrices. Some basic results for bilinear systems on SE(3) with linear and quadratic measurements are also derived. Three examples—GPS attitude estimation, needle tip location, and vision-based robot end-effector pose estimation—are presented to illustrate the framework.

Keywords

Particle filteringEuclidean groupLie group

Information

Type
Article
Information
Robotica , Volume 25 , Issue 6 , November 2007 , pp. 725 - 737
Copyright
Copyright © Cambridge University Press 2007

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