Geometric uncertainties in polyhedral object recognition

It is shown, by direct geometric construction, that previously published uncertainty bounds on the location of polygonal or polyhedral objects can be tightened considerably. The improvement of the bounds is a result of considering the cross-coupling between rotational and translation uncertainties in the interpretation of the sensor data. Both two-dimensional and three-dimensional uncertainty bounding are tested in simulation. The two-dimensional studies are implemented as part of a robotic system for tactile recognition. These implementations prove to be useful vehicles for exploring the computational issues involved in a detailed analysis of the geometric uncertainties present in model-based object recognition systems. Among other results it is found that rotational uncertainty is independent of the scale of the models, and translational uncertainty is highly dependent on the relative angles of the model components that are sensed.<
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