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Semi-AutomaticGenerationofTrainingData forNeuralNetworks
for6DPoseEstimationandRoboticGrasping
JohannesNikolausRauer,MohamedAburaia,WilfriedWo¨ber
FHTechnikumWien
{rauer,aburaia,woeber}@technikum-wien.at
Abstract. Machine-learning-based approaches for
poseestimationare trainedusingannotatedground-
truthdata– images showing theobjectand informa-
tion of its pose. In this work an approach to semi-
automatically generate 6Dpose-annotateddata, us-
ing a movable marker and an articulated robot, is
presented. A neural network for pose estimation is
trained using datasets varying in size and type. The
evaluation shows that small datasets recorded in the
targetdomainandsupplementedwithaugmentedim-
ages lead tomorerobust results thanlargersynthetic
datasets. The results demonstrate that amobilema-
nipulatorusing theproposedpose-estimationsystem
could be deployed in real-life logistics applications
to increase the levelofautomation.
1. Introduction
Production facilities have successfully deployed
classic fixed-programmed robots since the 1960s.
Due to their inability to perceive the environment,
such robots have mostly been used in mass produc-
tion, where a static setup can be assumed [8]. The
productionindustries’moveawayfrommassproduc-
tion towards highly customized goods requires in-
creased flexibility. Deploying mobile manipulators,
a combination of mobile and articulated robots, for
intra-logistical transport tasks, promises this desired
modularity [6]. Since the accuracy achieved by mo-
bile robot navigation is not sufficient to grasp ob-
jects, robots need sensors to perceive their surround-
ingsandautonomouslydetectobjects’poses[1]. The
most promising approaches for pose estimation are
machine-learning-based methods applied to camera
data [2]. Deep neural networks are trained using an-
notated ground-truth data – images showing the ob-
ject and information of its pose [4]. State-of-the-art
methods for creating such data use markers rigidly attached to the objects, which have to be removed in
cumbersome post-processing [3], or need human an-
notators that align 3D models to video-streams [5].
In this work an approach to semi-automatically gen-
erate6D-pose-annotated trainingdatausinganartic-
ulated robot ispresented.
2.Semi-AutomaticData-Generation
As shown in Figure 1 the object is placed in front
of the robotandafiducialmarker isputon it inade-
fined pose. The pose of the marker with respect to
thecamera iscomputedfromthecaptured imageand
used to calculate the pose of the object with respect
totherobot’sbase. Themarker iscapturedfrommul-
tiple perspectives and the mean pose is calculated to
minimizeerrorsof thecameracalibrationandmarker
detection. Afterwards the marker is removed (care
mustbetakenthat theobject isnotdisplaced)andthe
robotarmmovesaroundtheobject tocapture images
and associated object-pose data automatically. In or-
der to make the data also usable for training neural
networks for object detection, the object can be ren-
dered inavirtualenvironment tocalculatesegmenta-
tion masks. The design minimizes the extent of hu-
manlabor. It isonlynecessarytoplacethemarkeron
the object, capture images of it, and remove it again,
to enable recording of several thousand training im-
agesfullyautonomously. Drawbacksarethat thepro-
cess has to be repeated to cover the other half of the
orientation space and that the background is static.
However, this canbesolvedbydataaugmentation.
3.Results&Discussion
Multiple annotated datasets are created using the
proposedmethodandusedtotrainthedeep-learning-
based6DposeestimationsystemDOPE[7]. Thean-
notated training data is split into five equally sized
2
Joint Austrian Computer Vision and Robotics Workshop 2020
- Titel
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Herausgeber
- Graz University of Technology
- Ort
- Graz
- Datum
- 2020
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Abmessungen
- 21.0 x 29.7 cm
- Seiten
- 188
- Kategorien
- Informatik
- Technik