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.
Robust Pose Estimation With an Outlier Diagnosis Based on a Relaxation of Rigid Body Constraints
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.
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Lin, Y., Tu, X., Xi, F., and Chan, V. (October 30, 2012). "Robust Pose Estimation With an Outlier Diagnosis Based on a Relaxation of Rigid Body Constraints." ASME. J. Dyn. Sys., Meas., Control. January 2013; 135(1): 014502. https://doi.org/10.1115/1.4006624
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