STATISTICAL ESTIMATION FOR EXTERIOR ORIENTATION FROM LINE-TO-LINE CORRESPONDENCES

This paper presents a statistical estimation from which a new objectiv e function for exterior orientation from line correspondences is deriv ed. The objective function is based on the assumption that the underly ing noise model for the line correspondences is the Fisher distributio n. The assumption is appropriate for 3D orientation, is different from the underlying noise models for k pixels positions, and allows us to do a consistent estimation of the unknown parameters. The objective fu nction gives two important facts: its formulation and concept is diffe rent for that of previous work, and it automatically estimates six unk nown parameters simultaneously. As a result, it provides an optimal so lution and better accuracy. We design an experimental protocol to eval uate the performance of the new algorithm. The results of each experim ent shows that the new algorithm produces answers whose errors are 10% -20% less than the competing decoupled least squares algorithm.