Reducing drift in parametric motion tracking

Abstract
We develop a clas so f dif ferential motion trackers that automatically stabilize when in finite domains. Most dif- ferential trackers compute motion only relative to one previ- ous frame, accumulating errors indefinitely. We estimate pose changes between a set of past frames, and develop a proba- bilistic framework for integrating those estimates. We use an approximation to the posterior distribution of pose changes as an uncertainty model for parametric motion in order to help arbitrate the use of multiple base frames. We demonstrate this framework on a simple 2D translational tracker and a 3D, 6- degree of freedom tracker.

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