Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/310017 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Intelligent Manufacturing [ISSN:] 1572-8145 [Volume:] 33 [Issue:] 7 [Publisher:] Springer US [Place:] New York, NY [Year:] 2022 [Pages:] 2157-2165
Publisher: 
Springer US, New York, NY
Abstract: 
Automated driving in public traffic still faces many technical and legal challenges. However, automating vehicles at low speeds in controlled industrial environments is already achievable today. A reliable obstacle detection is mandatory to prevent accidents. Recent advances in convolutional neural network-based algorithms hypothetically allow the replacement of distance measuring laser scanners with common monocameras. In this paper, we present a photorealistic 3D simulated factory environment for testing vision-based obstacle detecting algorithms preceding field tests on the safety–critical system. We further test two obstacle detection methods employing state-of-the-art semantic segmentation and depth estimation in a range of challenging test scenarios. Both models performed well under common factory settings. Some edge cases, however, lead to vehicle crashes.
Subjects: 
Automated factory transport
Visual obstacle detection
Autonomous transport
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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