Optimal motion and structure estimation

The problem of estimating motion and structure of a rigid scene from two perspective monocular views is studied. The optimization approach presented is motivated by the following observations of linear algorithms: (1) for certain types of motion, even pixel-level perturbations (such as digitization noise) may override the information characterized by epipolar constraint; (2) existing linear algorithms do not use the constraints in the essential parameter matrix E in solving for this matrix. The authors present approaches to estimating errors in the optimal solutions, investigate the theoretical lower bounds on the errors in the solutions and compare them with actual errors, and analyze two types of algorithms of optimization: batch and sequential. The analysis and experiments show that, in general, a batch technique performs better than a sequential technique for any nonlinear problems. A recursive batch processing technique is proposed for nonlinear problems that require recursive estimation.