三维LMS运动估计的迭代算法和线性算法

Iterative and Linear Algorithms for 3D LMS Motion Estimation

本文阐述引入特征点集误差分布的最小均方（LMS）运动估计问题．最优运动估计本质上是6自由度的非线性问题，本文提出推广的质心重合定理和运动参数分解定理，将其简化成3自由度的纯旋转问题．我们给出最优运动参数估计的线性算法，兼有运动估计的最优性质和能够实时实现的优点；同时给出一个迭代算法，其性能也是最优的，并且收敛性能更好．

This paper addresses the problem of LMS motion estimation, in the presence of feature point uncertainties. Optimal motion estimation is substantially a nonlinear problem with 6 depees of freedom. In this paper a modified centroid coincidence theorem and a motion parameter decomposition theerem are introduced,which degenerate it into a pure rotation problem with only 3 degrees of freedom. We propose a linear algorithm as well as an iterative algorithm, both providing optimal estimations. The linear one can be implemented in realtime,however the iterative one has better convergence abilities.