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Background: Pose estimation of rigid objects is a practical challenge inoptical metrology and computer vision. In this paper a novelstochastic-geometrical modeling framework for object pose estimation ispresented based on observing multiple feature points. Methods: Thisstochastic-geometrical framework utilizes mixture models for the feature pointdensities in object space as well as for interpreting real measurements. Directadvantages of this approach are the avoidance to resolve individual featurecorrespondences and to incorporate correct stochastic dependencies inmulti-view applications. First, the general modeling framework is presented,second, a general algorithm for pose estimation is derived, and third, twoexample models for a camera setup as well as a lateration setup are presented.Results: The numerical experiments show the effectiveness of this modeling andgeneral algorithm by investigating four simulation scenarios for threedifferent observation systems, including the dependence on measurementresolution, object deformations as well as strong measurement noise. It can beconcluded that the probabilistic modeling of pose estimation based on mixturemodels can lead to accurate and robust pose estimations.

A Stochastic-Geometrical Framework for Object Pose Estimation based on Mixture Models Avoiding the Correspondence Problem