Probabilistic visual verification for robotic assembly manipulation
Name
Rus_Probabilistic visual.pdf
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4.36 MB
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Adobe PDF
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Author(s)
Choi, Changhyun
Rus, Daniela L
Date Issued
May 2016
Journal
2016 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Choi, Changhyun, and Daniela Rus. “Probabilistic Visual Verification for Robotic Assembly Manipulation.” 2016 IEEE International Conference on Robotics and Automation (ICRA) (May 2016).
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICRA.2016.7487786
