Projects:PointSetRigidRegistration
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Particle Filtering with Stochastic Dynamics for Point Set Registration
Typically, registration algorithms compute the transformations paramaters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem problem, in which the corresponding distribution can naturally be estimated using a particle filter. Moreover, we treat motion as a local variation in the pose parameters obatined from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence as well as provide a dynamical model of uncertainity.
Description
Algorithm
Project Status
Key Investigators
- Georgia Tech: Romeil Sandhu, Samuel Dambreville, Allen Tannenbaum
Publications
In press
- R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.