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RoPose-Real: Real World Dataset Acquisition for Data-Driven Industrial Robot Arm Pose Estimation | IEEE Conference Publication | IEEE Xplore

RoPose-Real: Real World Dataset Acquisition for Data-Driven Industrial Robot Arm Pose Estimation


Abstract:

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 ...Show More

Abstract:

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 [1], 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.
Date of Conference: 20-24 May 2019
Date Added to IEEE Xplore: 12 August 2019
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Conference Location: Montreal, QC, Canada

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