A Bayesian framework for geometric uncertainties handling

Details A Bayesian framework for geometric uncertainties handling A Bayesian framework for geometric uncertainties handling

May 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001aipc..568..579m&link_type=abstract

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 20th International Workshop. AIP Conference Proceedi

Computer Science

Robotics

Robotics, Data Analysis: Algorithms And Implementation, Data Management, Information Theory And Communication Theory

Scientific paper

We present a Bayesian CAD modeler for robotic applications. We describe the methodology we use to represent and handle uncertainties using probability distributions on the system's parameters and sensor measurements. We address the problem of the propagation of geometric uncertainties and how to take this propagation into account when solving inverse problems. The proposed approach may be seen as a generalization of constraint-based approaches where we express a constraint as a probability distribution instead of a simple equality or inequality. We also describe appropriate numerical algorithms used to apply this methodology. Using an example, we show how to apply our approach by providing simulation results using the implemented CAD modeler. .

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