Background: Pose estimation of rigid objects is a practical challenge in
optical metrology and computer vision. In this paper a novel
stochastic-geometrical modeling framework for object pose estimation is
presented based on observing multiple feature points. Methods: This
stochastic-geometrical framework utilizes mixture models for the feature point
densities in object space as well as for interpreting real measurements. Direct
advantages of this approach are the avoidance to resolve individual feature
correspondences and to incorporate correct stochastic dependencies in
multi-view applications. First, the general modeling framework is presented,
second, a general algorithm for pose estimation is derived, and third, two
example models for a camera setup as well as a lateration setup are presented.
Results: The numerical experiments show the effectiveness of this modeling and
general algorithm by investigating four simulation scenarios for three
different observation systems, including the dependence on measurement
resolution, object deformations as well as strong measurement noise. It can be
concluded that the probabilistic modeling of pose estimation based on mixture
models can lead to accurate and robust pose estimations.

基于多个特征点的随机几何建模框架用于刚体物体位姿估计，并提供一种避免解决单个特征对应和在多视角应用中引入正确随机依赖性的方法。通过数值实验验证了该模型的有效性和对不同观测系统的适用性，包括测量分辨率、物体变形和测量噪声的影响。可以得出结论，基于混合模型的概率建模可以实现准确和鲁棒的位姿估计。