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Received: 2007-01-14

Revision Accepted: 2007-06-22

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.10 P.1604~1613

10.1631/jzus.2007.A1604


Gaussian particle filter based pose and motion estimation


Author(s):  WU Xue-dong, SONG Zhi-huan

Affiliation(s):  State Key Lab. of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   woolcn@163.com

Key Words:  Gaussian particle filter (GPF), Pose and motion estimation, Line features, Monocular vision, Extended Kalman filter (EKF), Unscented Kalman filter (UKF), Dual quaternion


WU Xue-dong, SONG Zhi-huan. Gaussian particle filter based pose and motion estimation[J]. Journal of Zhejiang University Science A, 2007, 8(10): 1604~1613.

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author="WU Xue-dong, SONG Zhi-huan",
journal="Journal of Zhejiang University Science A",
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number="10",
pages="1604~1613",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A1604"
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%T Gaussian particle filter based pose and motion estimation
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%A SONG Zhi-huan
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%P 1604~1613
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%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1604

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T1 - Gaussian particle filter based pose and motion estimation
A1 - WU Xue-dong
A1 - SONG Zhi-huan
J0 - Journal of Zhejiang University Science A
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2007.A1604


Abstract: 
Determination of relative three-dimensional (3D) position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fields such as photogrammetry. A solution to pose and motion estimation problem that uses two-dimensional (2D) intensity images from a single camera is desirable for real-time applications. The difficulty in performing this measurement is that the process of projecting 3D object features to 2D images is a nonlinear transformation. In this paper, the 3D transformation is modeled as a nonlinear stochastic system with the state estimation providing six degrees-of-freedom motion and position values, using line features in image plane as measuring inputs and dual quaternion to represent both rotation and translation in a unified notation. A filtering method called the gaussian particle filter (GPF) based on the particle filtering concept is presented for 3D pose and motion estimation of a moving target from monocular image sequences. The method has been implemented with simulated data, and simulation results are provided along with comparisons to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) to show the relative advantages of the GPF. Simulation results showed that GPF is a superior alternative to EKF and UKF.

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