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This article describes an algorithm for pose or motion es- timation based on clustering of parameters in the six-dimensional pose space. The parameter samples are computed from data samples randomly drawn from stereo data points. The estimator is global and robust, per- forming matches to parts of a scene without prior pose information. It is general, in that it does not require any particular object features. Empirical object models can be built largely automatically. An imple- mented application from the service robotic domain and a quantitative performance study on real data are presented.

Pose Clustering From Stereo Data