An iterated estimation of the motion parameters of a rigid body from noisy displacement vectors

Concerns the estimation of the motion parameters of a rigid body from noisy point matches made on perspective views. A minimization problem which permits relatively robust parameter estimation and helps overcome poor zoom estimation when the field-of-view is small is formulated. It is assumed that the motion has a small rotary component, interframe differences are small, and the errors in the system are due to independently distributed errors in the components of the displacement vectors. A fast procedure for minimization is exposited, in which the parameter set is partitioned and conditional generalized least-squares formulas identified. Recursive application of these provides the search space for the minimization problem. Comparative results are presented using simulated data and displacement vectors obtained from an intensity-based matching algorithm