<div class="csl-bib-body">
<div class="csl-entry">Buchner, C., Gsellmann, P., Melik-Merkumians, M., & Schitter, G. (2023). Eye-In-Hand Pose Estimation of Industrial Robots. In <i>IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society</i>. 49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023), Singapur, Singapore. https://doi.org/10.1109/IECON51785.2023.10312053</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/190765
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dc.description.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.
en
dc.language.iso
en
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dc.subject
Industrial Robots
en
dc.subject
SLAM
en
dc.subject
Visual Tracking
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dc.subject
Performance Evaluations and Benchmarking
en
dc.title
Eye-In-Hand Pose Estimation of Industrial Robots
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.doi
10.1109/IECON51785.2023
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society
