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Semantic risk-aware costmaps for robots in industrial applications using deep learning on abstracted safety classes from synthetic data

  • 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.

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Metadaten
Author of HS ReutlingenWeber, Thomas; Danner, Michael; Rätsch, Matthias
URN:urn:nbn:de:bsz:rt2-opus4-35216
DOI:https://doi.org/10.5220/0010904100003124
ISBN:978-989-758-555-5
ISSN:2184-4321
Erschienen in: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
Publisher:SciTePress
Place of publication:Setúbal, Portugal
Document Type:Conference Proceeding
Language:English
Year of Publication:2022
Tag:data sets for robot learning; deep learning; detection and recognition; safety in human and robot interaction
Page Number:7
First Page:984
Last Page:990
DDC classes:600 Technik
Open Access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International