A Sensor Planning Method for Vision Guided 3D Surface Measurement and Reconstruction
【摘要】：正In this paper we present a novel sensor planning method for achieving efficient measurement and reconstruction of freeform object surfaces. Using the modified Bayesian Information Criterion (BIC), we first design a model selection strategy to obtain an optimal model structure for the freeform surface. Based on the selected model structure, we then determine a set of data points to be measured. B-splines are adopted for modeling the free form surface. In order to obtain more reliable parameter estimation for the B-spline model, we analyze the uncertainty of the model and use the statistical analysis of the Fisher information matrix to optimize the locations of the data points needed in the measurements. Using a cloud of data points of a surface acquired by a 3D vision system, we implemented the proposed method for reconstructing freeform surfaces.