creators_name: Mekhnacha, K creators_name: Mazer, E creators_name: Bessiere, P type: journalp datestamp: 2004-08-06 lastmod: 2011-03-11 08:55:39 metadata_visibility: show title: The Design and Implementation of a Bayesian CAD Modeler for Robotic Applications ispublished: pub subjects: comp-sci-robot full_text_status: public keywords: CAD Bayesian robotics abstract: We present a Bayesian CAD modeler for robotic applications. We address the problem of taking into account the propagation of geometric uncertainties when solving inverse geometric problems. The proposed method may be seen as a generalization of constraint-based approaches in which we explicitly model geometric uncertainties. Using our methodology, a geometric constraint is expressed as a probability distribution on the system parameters and the sensor measurements, instead of a simple equality or inequality. To solve geometric problems in this framework, we propose an original resolution method able to adapt to problem complexity. Using two examples, we show how to apply our approach by providing simulation results using our modeler. date: 2001 date_type: published publication: Advanced Robotics volume: 15 number: 1 refereed: FALSE citation: Mekhnacha, Dr K and Mazer, Dr E and Bessiere, Dr P (2001) The Design and Implementation of a Bayesian CAD Modeler for Robotic Applications. [Journal (Paginated)] document_url: http://cogprints.org/3739/1/Mekhnacha01.pdf