Multisensor-based object recognition using uncertain geometric models

One of the problems in sensor integration is how to design the integration strategy for the given task. The authors deal with model-based object recognition from uncertain geometric observations using uncertain object models. First, they decompose the recognition problem into a hierarchy of statistically well-defined subproblems depending on sensor uncertainties and model uncertainties. A recognition algorithm based on this approach is developed. Second, a method to preserve the consistency under model uncertainties is discussed. It is shown that information loss can be avoided by adding dummy variables to parameters in the integration. Finally, applications of the proposed method to 2D object recognition are demonstrated