A nonlinear optimization algorithm for the estimation of structure and motion parameters

A nonlinear least-squares optimization technique is proposed which uses the Levenberg-Marquardt method and estimates the motion and structure parameters to a global scale factor by minimizing an objective function. This objective function is the mean-square difference between the measured coordinates of feature points in the image plane and the coordinates predicted from the current state estimate. In comparison to existing approaches, this technique converges faster and yields better estimates. A recursive version of this algorithm is developed using the block approach. This algorithm is shown to also track eventful motion effectively. The performance of the proposed technique on real image sequences is also presented. Some performance results are indicated to illustrate the efficacy of this approach