Intelligent adaptive sampling guided by Gaussian process inference

Abstract

With the aim of reducing sampling density while having minimal impact on surface reconstruction accuracy, an adaptive sampling method based on Gaussian process inference is proposed. In each iterative step, the current sampling points serve as the training data to predict surface topography and then a new sampling point is adaptively located and inserted at the position where the maximum inference uncertainty is estimated. The updated samples are trained in the next step. By such an iterative training–inference–sampling approach, the reconstructed topography can converge to the expected one efficiently. Demonstrations on different structured, freeform and roughness surfaces ascertain the effectiveness of the sampling strategy. It can lead to an accurate inference of the surface topography and a sufficient reduction of data points compared with conventional uniform sampling. Robustness against random surface features, measurement noise and sharp height changes is further discussed. Such an adaptive sampling method is extremely suitable for discrete point-by-point measurements.