Impact of cross-section centers estimation on the accuracy of the point cloud spatial expansion using robust M-estimation and Monte Carlo simulation

The point cloud spatial expansion (PCSE) method creates an alternative form of representing the shape of symmetrical objects and introduces additional descriptive geometric parameters. An important element of the procedure is determining the course of the axis of symmetry of cylindrical objects based on cross-sections of point clouds. Outliers occurring in laser measurements are of great importance in this case. In this study, six robust estimation methods were used to determine the coordinates of the section centers. Accuracy analysis was performed both for data simulated with the Monte Carlo method and the real data. The study showed the advantage of robust methods for the PCSE method over the classical method of least squares estimation.

Automated summary

This study used six robust estimation methods to determine the course of the axis of symmetry of cylindrical objects and performed accuracy analysis. The results showed the advantage of robust methods over the classical method of least squares estimation in the PCSE method.