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RoPose-Real: real world dataset acquisition for data-driven industrial robot arm pose estimation

  • It is necessary to employ smart sensory systems in dynamic and mobile workspaces where industrial robots are mounted on mobile platforms. Such systems should be aware of flexible and non-stationary workspaces and able to react autonomously to changing situations. Building upon our previously presented RoPose-system, which employs a convolutional neural network architecture that has been trained on pure synthetic data to estimate the kinematic chain of an industrial robot arm system, we now present RoPose-Real. RoPose-Real extends the prior system with a comfortable and targetless extrinsic calibration tool, to allow for the production of automatically annotated datasets for real robot systems. Furthermore, we use the novel datasets to train the estimation network with real world data. The extracted pose information is used to automatically estimate the observing sensor pose relative to the robot system. Finally we evaluate the performance of the presented subsystems in a real world robotic scenario.

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Metadaten
Name:Gulde, Thomas; Ludl, Dennis; Andrejtschik, Johann; Thalji, Salma; Curio, Cristóbal
DOI:https://doi.org/10.1109/ICRA.2019.8793900
ISBN:978-1-5386-6027-0
Erschienen in:2019 International Conference on Robotics and Automation (ICRA)
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Year of Publication:2019
Pagenumber:7
First Page:4389
Last Page:4395
Dewey Decimal Classification:004 Informatik
Open Access:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE