MATLAB Implementation of Plane Fitting Methods with Code Description

MATLAB plane fitting methods comprise computer algorithms based on the MATLAB programming language, designed for performing plane fitting on datasets. The implementation process involves several key steps: data input handling using functions like load() or readtable(), establishment of plane equations through linear regression techniques typically implemented with the backslash operator (\) for solving least-squares problems, and error calculation using residual analysis with functions such as norm() or custom error metrics. This method enables more accurate data fitting and enhances data analysis precision by minimizing the perpendicular distance between data points and the fitted plane.

Furthermore, MATLAB plane fitting methods find diverse applications across multiple domains. They are extensively used in computer vision for surface reconstruction, in robotics control for environment mapping, and in 3D scanning for point cloud processing. The algorithm typically employs singular value decomposition (SVD) or principal component analysis (PCA) through functions like svd() to determine the optimal plane normal vector. Mastering this algorithm not only improves data analysis accuracy but also expands application possibilities, providing substantial support for research in related fields through customizable implementation options.

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