TY  - JOUR
A1  - Kunkel, Maximilian Hugo
A1  - Gebhardt, Andreas
A1  - Mpofu, Khumbulani
A1  - Kallweit, Stephan
T1  - Quality assurance in metal powder bed fusion via deep-learning-based image classification
JF  - Rapid Prototyping Journal
Y1  - 2019
U6  - https://doi.org/10.1108/RPJ-03-2019-0066
SN  - 1355-2546
VL  - 26
IS  - 2
SP  - 259
EP  - 266
ER  - 
TY  - CHAP
A1  - Schleupen, Josef
A1  - Engemann, Heiko
A1  - Bagheri, Mohsen
A1  - Kallweit, Stephan
A1  - Dahmann, Peter
T1  - Developing a climbing maintenance robot for tower and rotor blade service of wind turbines
T2  - Advances in Robot Design and Intelligent Control : Proceedings of the 25th Conference on Robotics in Alpe-Adria-Danube Region (RAAD16)
Y1  - 2017
SN  - 978-3-319-49058-8
U6  - https://doi.org/10.1007/978-3-319-49058-8_34
N1  - Advances in Robot Design and Intelligent Control ; Vol. 540
SP  - 310
EP  - 319
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Ferrein, Alexander
A1  - Schiffer, Stefan
A1  - Kallweit, Stephan
T1  - The ROSIN Education Concept - Fostering ROS Industrial-Related Robotics Education in Europe
T2  - ROBOT 2017: Third Iberian Robotics Conference
Y1  - 2018
SN  - 978-3-319-70836-2
U6  - https://doi.org/10.1007/978-3-319-70836-2_31
N1  - Advances in Intelligent Systems and Computing, vol 694;
(AISC, volume 694)
SP  - 370
EP  - 381
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Engemann, Heiko
A1  - Du, Shengzhi
A1  - Kallweit, Stephan
A1  - Ning, Chuanfang
A1  - Anwar, Saqib
T1  - AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation
T2  - Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020
N2  - We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale-CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level.
Y1  - 2020
SN  - 978-1-64368-137-5
U6  - https://doi.org/10.3233/FAIA200770
N1  - Frontiers in Artificial Intelligence and Applications. Vol 332
SP  - 89
EP  - 97
PB  - IOS Press
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Engemann, Heiko
A1  - Du, Shengzhi
A1  - Kallweit, Stephan
A1  - Cönen, Patrick
A1  - Dawar, Harshal
T1  - OMNIVIL - an autonomous mobile manipulator for flexible production
JF  - Sensors
Y1  - 2020
SN  - 1424-8220
U6  - https://doi.org/10.3390/s20247249
N1  - Special issue: Sensor Networks Applications in Robotics and Mobile Systems
VL  - 20
IS  - 24, art. no. 7249
SP  - 1
EP  - 30
PB  - MDPI
CY  - Basel
ER  -