TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Weber, Thomas
A1  - Danner, Michael
A1  - Zhang, Bo
A1  - Rätsch, Matthias
A1  - Zell, Andreas
T1  - Semantic risk-aware costmaps for robots in industrial applications using deep learning on abstracted safety classes from synthetic data
T2  - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, 6-8 February 2022, virtual event
N2  - For collision and obstacle avoidance as well as trajectory planning, robots usually generate and use a simple 2D costmap without any semantic information about the detected obstacles. Thus a robot’s path planning will simply adhere to an arbitrarily large safety margin around obstacles. A more optimal approach is to adjust this safety margin according to the class of an obstacle. For class prediction, an image processing convolutional neural network can be trained. One of the problems in the development and training of any neural network is the creation of a training dataset. The first part of this work describes methods and free open source software, allowing a fast generation of annotated datasets. Our pipeline can be applied to various objects and environment settings and is extremely easy to use to anyone for synthesising training data from 3D source data. We create a fully synthetic industrial environment dataset with 10 k physically-based rendered images and annotations. Our da taset and sources are publicly available at https://github.com/LJMP/synthetic-industrial-dataset. Subsequently, we train a convolutional neural network with our dataset for costmap safety class prediction. We analyse different class combinations and show that learning the safety classes end-to-end directly with a small dataset, instead of using a class lookup table, improves the quantity and precision of the predictions.
KW  - data sets for robot learning
KW  - deep learning
KW  - safety in human and robot interaction
KW  - detection and recognition
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-35216
SN  - 2184-4321
SS  - 2184-4321
SN  - 978-989-758-555-5
SB  - 978-989-758-555-5
U6  - https://doi.org/10.5220/0010904100003124
DO  - https://doi.org/10.5220/0010904100003124
SP  - 984
EP  - 990
S1  - 7
PB  - SciTePress
CY  - Setúbal, Portugal
ER  -