Probabilistic visual verification for robotic assembly manipulation
Author(s)Choi, Changhyun; Rus, Daniela L
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In this paper we present a visual verification approach for robotic assembly manipulation which enables robots to verify their assembly state. Given shape models of objects and their expected placement configurations, our approach estimates the probability of the success of the assembled state using a depth sensor. The proposed approach takes into account uncertainties in object pose. Probability distributions of depth and surface normal depending on the uncertainties are estimated to classify the assembly state in a Bayesian formulation. The effectiveness of our approach is validated in comparative experiments with other approaches.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016 IEEE International Conference on Robotics and Automation (ICRA)
Institute of Electrical and Electronics Engineers (IEEE)
Choi, Changhyun, and Daniela Rus. “Probabilistic Visual Verification for Robotic Assembly Manipulation.” 2016 IEEE International Conference on Robotics and Automation (ICRA) (May 2016).
Author's final manuscript