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Technical Briefs

Robust Pose Estimation With an Outlier Diagnosis Based on a Relaxation of Rigid Body Constraints

[+] Author and Article Information
Yu Lin

Graduate Student
Mem. ASME
Department of Aerospace Engineering,
Ryerson University,
Toronto, ON, M5B 2K3, Canada
e-mail: yu.lin@ryerson.ca

Xiao-wei Tu

Research Officer
Aerospace Manufacturing Technology Center,
National Research Council Canada,
Montreal, QC, H3T 2B2, Canada
e-mail: xiao-wei.tu@cnrc-nrc.gc.ca

Fengfeng Xi

Professor
Department of Aerospace Engineering,
Ryerson University,
Toronto, ON, M5B 2K3, Canada
e-mail: fengxi@ryerson.ca

Vincent Chan

Professor
Mem. ASME
Department of Mechanical and Industrial Engineering,
Ryerson University,
Toronto, ON, M5B 2K3, Canada
e-mail: v7chan@ryerson.ca

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received August 27, 2010; final manuscript received March 11, 2012; published online October 30, 2012. Assoc. Editor: Sheng-Guo Wang.

J. Dyn. Sys., Meas., Control 135(1), 014502 (Oct 30, 2012) (6 pages) Paper No: DS-10-1247; doi: 10.1115/1.4006624 History: Received August 27, 2010; Revised March 11, 2012

In this paper, we propose a novel outlier diagnosis method for robust pose estimation of rigid body motions from outlier contaminated 3D point measurements. Due to incorrect correspondences in a cluttered measuring environment, observed point data are contaminated by outliers, which are unusual gross errors that lie out of an overall error distribution. Standard least-squares methods for pose estimation are highly sensitive to outliers. For this reason, an outlier diagnosis method is developed to preprocess measured point data prior to pose estimation. This diagnosis method detects and removes outliers based on a relaxation method with rigid body constraints of a rigid body. Simulations and experiments prove the effectiveness and advantages of high breakdown point and ease of implementation.

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References

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Figures

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Fig. 1

Motion of the cubic rigid body, including a translation of t and a rotation of R

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Fig. 2

Experimental system setup [14]

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Fig. 3

(a) Cubic rigid body and seven markers and (b) two poses of the cubic rigid body

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Fig. 4

Outliers occurred at markers 1, 2, 6, and 7: (a) transformation determined with four outliers and (b) pose estimation from the remaining three points after removing the outliers

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Fig. 5

(a) Merit scores during seven iterations and (b) signs obtained from normalized merit scores, “−1” indicate outliers (markers 1, 2, 6, and 7), and “+1” indicate the reliable data (markers 3, 4, and 5)

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Fig. 6

Markers for simulating ouliers and the original markers

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