Abstract
Abstract Estimated transformation parameters based on the three-dimensional (3D) similarity datum transformation are affected or even severely distorted when the observed coordinates are contaminated by gross errors or outliers. In our paper, the problem of 3D similarity datum transformation is described as a generalized Errors-In-Variables (EIV) model based on a least squares solution. Then, an advanced multiple outlier detection algorithm that uses the L1 penalty function on the mean-shift model is proposed in the generalized EIV model. A general thresholding rule and a least trimmed squares estimator are invoked in the proposed algorithm. The results of the real and simulated experiments indicate that the proposed algorithm can effectively reduce the influence of multiple outliers and yield reliable transformation parameters compared with the iteratively reweighted total least squares, data snooping and robust outlier removal approaches.
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