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Figure1.Procedure forgeneratingannotated data, usinga robot andamovable fiducialmarker.
portionsandmerged togaindatasetscontaining20%
to100%(15k images)of all recordedsamples.
The translational-15mm-errormetrics (percentage
of tested data for which the translational error is
smaller than 15 mm – accuracy necessary for grasp-
ing)[7] inFigure2show, thatusingpre-trainedmod-
els (blue, 6-10) leads to better performance than ini-
tializing networks with random weights (red, 1-5).
Bigger datasets do not necessarily improve the ac-
curacy since biased datasets lead to wrong general-
izations (e.g. network 5). A relatively small dataset
recorded in the target domain achieves better results
than a several times larger synthetic dataset (net-
work 12: 15k real + 15k domain randomized im-
ages), especially when extended using data augmen-
tation (network 11: smallest real dataset augmented
twice). Therotationalerrorsshowsimilar results,but
are generally lower.
Figure 2. Translational errors compared regarding train-
ing time: Synthetic data (green), augmented data (cyan),
pre-trained (blue) and non-pre-trained networks (red).
Bubble-sizevisualizes dataset-size. A qualitative evaluation using a real mobile ma-
nipulatorconfirms that theproposedpose-estimation
system could be deployed in real-life logistics appli-
cations to increase the levelof automation.
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3
Joint Austrian Computer Vision and Robotics Workshop 2020
- Title
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Editor
- Graz University of Technology
- Location
- Graz
- Date
- 2020
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Categories
- Informatik
- Technik