Three-dimensional point cloud registration by matching surface features with relaxation labeling method

Automated approaches for the conversion of multiple overlapped three-dimensional (3D) point clouds into an integrated surface shape measurement in the form of a complete polygon surface are important in the general field of reverse engineering. Traditionally, the conversion process is achieved in a semi-automated manner that requires extensive user interaction. In this work, automated methods for point set registration are developed and experimentally validated using polygon surface reconstruction to represent raw, 3D point clouds obtained from non-contacting measurement systems. Using local differential properties extracted from the polygon surface representation for a measurement data set, a robust sculpture surface feature-matching method is described for automatically obtaining the initial orientation and mismatch estimates for each overlapped data set. Using both simulated and measured experimental data to quantify the performance of the method, it is shown that differential local surface features are appropriate metrics for identifying common features and initializing the relative positions of individual point clouds, thereby providing the basis for automating the registration and integration processes while improving the speed of the surface distance minimization method developed for the initial registration process.