Testing the reliability of monocular obstacle detection methods in a simulated 3D factory environment

Marius Wenning (), Anton Akira Backhaus (), Tobias Adlon () and Peter Burggräf ()
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Marius Wenning: Werkzeugmaschinenlabor, RWTH Aachen University
Anton Akira Backhaus: Werkzeugmaschinenlabor, RWTH Aachen University
Tobias Adlon: Werkzeugmaschinenlabor, RWTH Aachen University
Peter Burggräf: Werkzeugmaschinenlabor, RWTH Aachen University

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 7, No 16, 2157-2165

Abstract: 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.

Keywords: Automated factory transport; Visual obstacle detection; Autonomous transport (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10845-022-01983-4

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