The design and implementation of a Bayesian CAD modeler for robotic applications

We present a Bayesian CAD modeler for robotic applications. We address the problem of taking into account the propagation of geometric uncertainties w hen solving inverse geometric problems. The proposed method may be seen as a generalization of constraint-based approaches in which we explicitly mode l geometric uncertainties. Using our methodology, a geometric constraint is expressed as a probability distribution on the system parameters and the s ensor measurements, instead of a simple equality or inequality. To solve ge ometric problems in this framework, we propose an original resolution metho d able to adapt to problem complexity. Using two examples, we show how to a pply our approach by providing simulation results using our modeler.