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Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)

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  • Guanglei Li
  • Yahui Cui
  • Lihua Wang
  • Lei Meng

Abstract

To improve the accurate and sufficient recognition of abnormal points on the workpiece, a multidimensional anomaly point identification approach based on an improved eigenvalue method is proposed in this paper. Whether a point is normal or not depends on the angle between the two adjacent vectors which consisted of four adjacent points around the current focus. The comprehensive judgment is carried out by multidimensional approximation. The numerical simulation and actual experiment validate the efficiency of the proposed method to quickly and accurately identify the abnormal point cloud in the surface point cloud data.

Suggested Citation

  • Guanglei Li & Yahui Cui & Lihua Wang & Lei Meng, 2020. "Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, April.
    • Handle: RePEc:hin:jnlmpe:4712916
    DOI: 10.1155/2020/4712916
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