Record link: 
http://hdl.handle.net/20.500.12708/190765
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Title: 
Eye-In-Hand Pose Estimation of Industrial Robots
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Citation: 
Buchner, C., Gsellmann, P., Melik-Merkumians, M., & Schitter, G. (2023). Eye-In-Hand Pose Estimation of Industrial Robots. In IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. 49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023), Singapur, Singapore. https://doi.org/10.1109/IECON51785.2023.10312053
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Publisher DOI: 
10.1109/IECON51785.2023.10312053
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Buchner, Christoph 
Gsellmann, Peter 
Melik-Merkumians, Martin 
Schitter, Georg 
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Organisational Unit: 
E376-01 - Forschungsbereich Intelligente Mechatronische Systeme 
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Published in: 
IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society 
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DOI of the book: 
10.1109/IECON51785.2023
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Date (published): 
18-Oct-2023
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Event name: 
49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023) 
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Event date: 
16-Oct-2023 - 19-Oct-2023
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Event place: 
Singapur, Singapore
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Number of Pages: 
6
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Peer reviewed: 
Yes
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Keywords: 
Industrial Robots; SLAM; Visual Tracking; Performance Evaluations and Benchmarking
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Abstract: 
This paper proposes a pose estimation approach of an industrial six-degree-of-freedom robot without the need of externally placed sensors. An RGB-D camera that is placed at the robots end-effector is combined with a SLAM algorithm to act as a sensor system. The presented system based on the novel integration approach is evaluated by performing test trajectories and is capable of estimating the tool-center point with a standard deviation of 2.6 mm and performing a joint state estimation with a standard deviation of 13.6 mrad without any external sensor.
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Research Areas: 
Mathematical and Algorithmic Foundations: 50%
Sensor Systems: 50%
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Science Branch: 
2020 - Elektrotechnik, Elektronik, Informationstechnik: 100%
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Appears in Collections:
Conference Paper

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