| DOI | Resolve DOI: https://doi.org/10.1115/1.4006624 |
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| Author | Search for: Lin, Y.; Search for: Tu, X.-W.1; Search for: Xi, F.; Search for: Chan, V. |
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| Affiliation | - National Research Council of Canada. Aerospace
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| Format | Text, Article |
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| Subject | 3D point measurement; Diagnosis methods; Error distributions; Gross errors; High breakdown point; Least squares methods; Measured points; Point data; Pose estimation; Preprocess; Relaxation methods; Rigid body; Rigid-body motion; Large eddy simulation; Rigid structures; Statistics |
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| Abstract | 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. © 2013 American Society of Mechanical Engineers. |
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| Publication date | 2013 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 21269634 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 2613a27b-cce4-4512-a2bb-c6535213cd45 |
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| Record created | 2013-12-13 |
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| Record modified | 2020-04-22 |
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