Dynamic Estimation of Rigid Motion from Perspective Views via Recursive Identification of Exterior Differential Systems with Parameters on a Topological Manifold
CALIFORNIA INST OF TECH PASADENA CONTROL AND DYNAMICAL SYSTEMS
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We formulate the problem of estimating the motion of a rigid object viewed under perspective projection as the identification of a dynamic model in Exterior Differential form with parameters on a topological manifold. We first describe a general method for recursive identification of nonlinear implicit systems using prediction error criteria. The parameters are allowed to move slowly on some topological not necessarily smooth manifold. The basic recursion is solved in two different ways one is based on a simple extension of the traditional Kalman Filter to nonlinear and implicit measurement constraints, the other may be regarded as a generalized Gauss-Newton iteration, akin to traditional Recursive Prediction Error Method techniques in linear identification. A derivation of the Implicit Extended Kalman Filter IEKF is reported in the appendix. The ID framework is then applied to solving the visual motion problem it indeed is possible to characterize it in terms of identification of an Exterior Differential System with parameters living on a Co topological manifold, called the essential manifold. We consider two alternative estimation paradigms. The first is in the local coordinates of the essential manifold we estimate the state of a nonlinear implicit model on a linear space. The second is obtained by a linear update on the linear embedding space followed by a projection onto the essential manifold. These schemes proved successful in performing the motion estimation task, as we show in experiments on real and noisy synthetic image sequences.
- Numerical Mathematics
- Theoretical Mathematics
- Anatomy and Physiology