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Semantic Risk-aware Costmaps for Robots in Industrial Applications using Deep Learning on Abstracted Safety Classes from Synthetic Data

Topics: Categorization and Scene Understanding; Deep Learning for Visual Understanding ; Human and Computer Interaction; Mobile and Egocentric Object Detection and Recognition; Vision for Robotics

Authors: Thomas Weber 1 ; Michael Danner 1 ; Bo Zhang 2 ; Matthias Rätsch 1 and Andreas Zell 3

Affiliations: 1 Reutlingen Research Institute, Reutlingen University, Alteburgstrasse 150, 72762 Reutlingen, Germany ; 2 School of Electronics Information, Xi’an Polytechnic University, Xi’an, China ; 3 Cognitive Systems, Eberhard-Karls-University Tübingen, 72076 Tübingen, Germany

Keyword(s): Data Sets for Robot Learning, Deep Learning, Safety in Human and Robot Interaction, Detection and Recognition.

Abstract: 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. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Weber, T.; Danner, M.; Zhang, B.; Rätsch, M. and Zell, A. (2022). Semantic Risk-aware Costmaps for Robots in Industrial Applications using Deep Learning on Abstracted Safety Classes from Synthetic Data. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-555-5; ISSN 2184-4321, pages 984-990. DOI: 10.5220/0010904100003124

@conference{visapp22,
author={Thomas Weber. and Michael Danner. and Bo Zhang. and Matthias Rätsch. and Andreas Zell.},
title={Semantic Risk-aware Costmaps for Robots in Industrial Applications using Deep Learning on Abstracted Safety Classes from Synthetic Data},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2022},
pages={984-990},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010904100003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Semantic Risk-aware Costmaps for Robots in Industrial Applications using Deep Learning on Abstracted Safety Classes from Synthetic Data
SN - 978-989-758-555-5
IS - 2184-4321
AU - Weber, T.
AU - Danner, M.
AU - Zhang, B.
AU - Rätsch, M.
AU - Zell, A.
PY - 2022
SP - 984
EP - 990
DO - 10.5220/0010904100003124