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.
|Original language||English (US)|
|Title of host publication||2016 IEEE International Conference on Robotics and Automation, ICRA 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - Jun 8 2016|
|Event||2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden|
Duration: May 16 2016 → May 21 2016
|Name||Proceedings - IEEE International Conference on Robotics and Automation|
|Other||2016 IEEE International Conference on Robotics and Automation, ICRA 2016|
|Period||5/16/16 → 5/21/16|
Bibliographical noteFunding Information:
This work was supported by the Boeing Company
© 2016 IEEE.