Abstract
We introduce a vision system that is able to on-line learn spatial constraints to improve pose estimation in terms of correct recognition as well as computational speed. By making use of a simulated industrial robot system performing various pick and place tasks, we show the effect of model building when making use of visual knowledge in terms of visually extracted pose hypotheses as well as action knowledge in terms of pose hypotheses verified by action execution. We show that the use of action knowledge significantly improves the pose estimation process.
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Acknowledgments
The research leading to these results has received funding from the European Communities Seventh Framework Programme FP7/2007-2013 (Programme and Theme: ICT-2011.2.1, Cognitive Systems and Robotics) under grant agreement no. 600578, ACAT and by Danish Agency for Science, Technology and Innovation, project CARMEN. We thank Dimitris Chrysostomou and Ole Madsen for their support.
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Meyer, K.K. et al. (2017). Using Online Modelled Spatial Constraints for Pose Estimation in an Industrial Setting. In: Zhang, D., Wei, B. (eds) Mechatronics and Robotics Engineering for Advanced and Intelligent Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-33581-0_10
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DOI: https://doi.org/10.1007/978-3-319-33581-0_10
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