Error Correction for Laser Tracking System Using Dynamic Weighting Model

Liu, W. L.; Wang, Zhankui; Wang, Shibo; Li, Xiaoyang

doi:10.1155/2013/869406

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…As the laser tracker is a spherical coordinates measuring equipment, its measurement uncertainty does not conform to the x -, y - and z -axis representations. In general, the covariance matrix of measurement can be deduced using the Jacobian matrix of measuring equations and variance-covariance propagation law (Zhang and Qu, 2012; Liu et al ., 2013; Mitchell et al ., 2004). The uncertainty ellipsoid based method is an alternative approach of Jacobian matrix method, which was presented in Predmore (2010) and Huang et al .…”

Section: Covariance Matrix Of Enhanced Reference System Pointsmentioning

confidence: 99%

“…(2013), who claimed that neither the zenith angle nor the azimuth angle have effects on the size of uncertainty ellipsoid. Another approach to derive the covariance matrix of measurement is the Jacobian matrix method (Zhang and Qu, 2012; Liu et al ., 2013; Mitchell et al ., 2004). By introducing a simple matrix transformation, it is proved that our result is the same as that of Jacobian matrix method.…”

Section: Covariance Matrix Of Enhanced Reference System Pointsmentioning

confidence: 99%

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Uncertainties evaluation of coordinate transformation parameters in the large-scale measurement for aircraft assembly

Deng

Huang

2018

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Purpose For the measurement of large-scale components in aircraft assembly, the evaluation of coordinate transformation parameters between the coordinate frames of individual measurement systems to the assembly frame is an essential task, which is usually completed by registration of the enhanced reference system (ERS) points. This paper aims to propose an analytical method to evaluate the uncertainties of transformation parameters considering both the measurement error and the deployment error of ERS points. Design/methodology/approach For each measuring station, the measured coordinates of ERS points are first roughly registered to the assembly coordinate system using the singular value decomposition method. Then, a linear transformation model considering the measurement error and deployment error of ERS points is developed, and the analytical solution of transformation parameters’ uncertainties is derived. Moreover, the covariance matrix of each ERS points in the transformation evaluation is calculated based on a new uncertainty ellipsoid model and variance-covariance propagation law. Findings For the transformation of both single and multiple measuring stations, the derived uncertainties of transformation parameters by the proposed analytical method are identical to that obtained by the state-of-the-art iterative method, but the solution process is simpler, and the computation expenses are much less. Originality/value The proposed uncertainty evaluation method would be useful for in-site measurement and optimization of the configuration of ERS points in the design of fixture and large assembly field. It could also be applied to other registration applications with errors on both sides of registration points.

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Section: Covariance Matrix Of Enhanced Reference System Pointsmentioning

confidence: 99%

Section: Covariance Matrix Of Enhanced Reference System Pointsmentioning

confidence: 99%

Uncertainties evaluation of coordinate transformation parameters in the large-scale measurement for aircraft assembly

Deng

Huang

2018

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“…However, when these measurements are combined, the coordinate transformation is used to unify the coordinate frames. The coordinate transformation methods usually introduce an uncertainty error into the measurement results; therefore, the coordinate transformation uncertainty directly affects the accuracy of the final measurement results [1][2][3][4].…”

Section: Measurement Science and Technologymentioning

confidence: 99%

A novel method for improving the accuracy of coordinate transformation in multiple measurement systems

Liu

2017

Meas. Sci. Technol.

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Large-scale dimensional metrology usually requires a combination of multiple measurement systems, such as laser tracking, total station, laser scanning, coordinate measuring arm and video photogrammetry, etc. Often, the results from different measurement systems must be combined to provide useful results. The coordinate transformation is used to unify coordinate frames in combination; however, coordinate transformation uncertainties directly affect the accuracy of the final measurement results. In this paper, a novel method is proposed for improving the accuracy of coordinate transformation, combining the advantages of the best-fit least-square and radial basis function (RBF) neural networks. First of all, the configuration of coordinate transformation is introduced and a transformation matrix containing seven variables is obtained. Second, the 3D uncertainty of the transformation model and the residual error variable vector are established based on the best-fit least-square. Finally, in order to optimize the uncertainty of the developed seven-variable transformation model, we used the RBF neural network to identify the uncertainty of the dynamic, and unstructured, owing to its great ability to approximate any nonlinear function to the designed accuracy. Intensive experimental studies were conducted to check the validity of the theoretical results. The results show that the mean error of coordinate transformation decreased from 0.078 mm to 0.054 mm after using this method in contrast with the GUM method.

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“…However, inconsistencies are usually found between laser scanning and inertial measurements, and hence the combination has not delivered satisfactory results in existing LSINS. Biased estimation and filtering divergence errors are consequently generated and decrease the navigation accuracy (Xiong et al, 2009; Liu et al, 2013; Kim et al, 2012). For example, Hesch et al (2016) presented an Extended Kalman Filter (EKF) to fuse information from an IMU and a Two-Dimension (2D) laser scanner to concurrently estimate the six DOF position and attitude of a moving object with respect to a Three-Dimension (3D) map of the environment.…”

Section: Introductionmentioning

confidence: 99%

Error Modelling and Optimal Estimation of Laser Scanning Aided Inertial Navigation System in GNSS-Denied Environments

Liu

Zhang

2018

J. Navigation

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A Laser Scanning aided Inertial Navigation System (LSINS) is able to provide highly accurate position and attitude information by aggregating laser scanning and inertial measurements under the assumption that the rigid transformation between sensors is known. However, a LSINS is inevitably subject to biased estimation and filtering divergence errors due to inconsistent state estimations between the inertial measurement unit and the laser scanner. To bridge this gap, this paper presents a novel integration algorithm for LSINS to reduce the inconsistences between different sensors. In this new integration algorithm, the Radial Basis Function Neural Networks (RBFNN) and Singular Value Decomposition Unscented Kalman Filter (SVDUKF) are used together to avoid inconsistent state estimations. Optimal error estimation in the LSINS integration process is achieved to reduce the biased estimation and filtering divergence errors through the error state and measurement error model built by the proposed method. Experimental tests were conducted to evaluate the navigation performance of the proposed method in Global Navigation Satellite System (GNSS)-denied environments. The navigation results demonstrate that the relationship between the laser scanner coordinates and the inertial sensor coordinates can be established to reduce sensor measurement inconsistencies, and LSINS position accuracy can be improved by 23·6% using the proposed integration method compared with the popular Extended Kalman Filter (EKF) algorithm.

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Error Correction for Laser Tracking System Using Dynamic Weighting Model

Cited by 5 publications

References 13 publications

Uncertainties evaluation of coordinate transformation parameters in the large-scale measurement for aircraft assembly

Uncertainties evaluation of coordinate transformation parameters in the large-scale measurement for aircraft assembly

A novel method for improving the accuracy of coordinate transformation in multiple measurement systems

Error Modelling and Optimal Estimation of Laser Scanning Aided Inertial Navigation System in GNSS-Denied Environments

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