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The Residuals Iteration Correction Algorithm of Exceptional Corresponding Points Based on Affine

Journal of the Indian Society of Remote Sensing volume 42, pages257–266(2014)Cite this article

Abstract

The corresponding points are prerequisite to the registration of Terrestrial Laser Scanning data (TLS). The exceptional corresponding points will direct impact the quality of registration. The interest in this paper is in the so-called residuals iteration correction algorithm, which focused on a new procedure for correcting the exceptional corresponding points. The kernel of the procedure is the Affine proposed by Berger (1987). This paper describes the three main steps of residuals iteration correction algorithm based on Affine, namely the decomposition of exceptional corresponding points, the propagation of registration residuals, and the correction of exceptional corresponding points. The paper outlines the key advantages of the proposed approach, such as the capability to correct exceptional corresponding point automatically according to the point precision. Furthermore, it illustrates the performance of proposed approach with a validation experiment where two exceptional corresponding points were simulated and “3S” statue TLS data in Wuhan University was acquired. From the analysis of this experiment, the result shows that the validation of correction of exceptional corresponding points based on residuals iteration.

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Author information

Affiliations

  1. School of Geodesy & Geomatics, Wuhan University, 129 Luoyu Road, Wuhan, China
    • Xijiang Chen
    • , Xianghong Hua
    •  & Jianjun Jiang
  2. Hazard monitoring & prevention Research Center, Wuhan University, Wuhan, China
    • Xijiang Chen
    • , Xianghong Hua
    •  & Jianjun Jiang
  3. Jiangxi Electric Power Design Institute, Nanchang, Jiangxi, China
    • Cheng Wei
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  1. Xijiang Chen
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Correspondence to Xianghong Hua.

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Chen, X., Hua, X., Jiang, J. et al. The Residuals Iteration Correction Algorithm of Exceptional Corresponding Points Based on Affine. J Indian Soc Remote Sens 42, 257–266 (2014). https://doi.org/10.1007/s12524-013-0320-1

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Keywords

  • Point cloud registration
  • Affine
  • Six parameters
  • Corresponding point
  • Outlier