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

• Published in: Measurement : journal of the International Measurement Confederation 2022-02, Vol.189, p.110436, Article 110436
• Main Authors: Dąbrowski, Paweł S., Hubert Zienkiewicz, Marek
• Format: Article
• Language: English
• Subjects: Cloud computing; Cross-sections; Estimating techniques; Lasers; Least squares method; Monte Carlo simulation; Outliers (statistics); PCSE; Point cloud; Robust estimation; Robustness; Simulation; Spatial expansion
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2021.110436

•Assessment of the accuracy of the point cloud spatial expansion method.•Analysis of cross-sections impact on the axis of symmetry.•Least-squares method and seven robust estimation methods applied.•Monte Carlo simulation and real-data case study analyses.•Various applicability of robust estimation methods. 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.