Robust pose estimation with an outlier diagnosis based on a relaxation of rigid body constraints

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DOIResolve DOI: http://doi.org/10.1115/1.4006624
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TypeArticle
Journal titleJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
ISSN0022-0434
Volume135
Issue1
Article number14502
Subject3D 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
AbstractIn 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.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); Aerospace (AERO-AERO)
Peer reviewedYes
NPARC number21269634
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Record identifier2613a27b-cce4-4512-a2bb-c6535213cd45
Record created2013-12-13
Record modified2016-05-09
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