RoPose-Real: Real World Dataset Acquisition for Data-Driven Industrial Robot Arm Pose Estimation
Thomas Gulde, Dennis Ludl, Johann Andrejtschik, Salma Thalji, Cristóbal Curio
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.
BibTeX
@inproceedings{icra2019_roposerealrealwo,
title = {RoPose-Real: Real World Dataset Acquisition for Data-Driven Industrial Robot Arm Pose Estimation},
author = {Thomas Gulde and Dennis Ludl and Johann Andrejtschik and Salma Thalji and Cristóbal Curio},
booktitle = {ICRA 2019},
year = {2019}
}