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October 1, 2021
Abstract
Image processing techniques are widely used within automotive series production, including production of electronic control units (ECUs). Deep learning approaches have made rapid advances during the last years, but are not prominent in those industrial settings yet. One major obstacle is the lack of suitable training data. We adapt the recently developed method of domain randomization to our use case of 3D pose estimation of ECU housings. We create purely synthetic data with high visual diversity to train artificial neural networks (ANNs). This enables ANNs to estimate the 3D pose of a real sample part with high accuracy from a single low-resolution RGB image in a production-like setting. Requirements regarding measurement hardware are very low. Our entire setup is fully automated and can be transferred to related industrial use cases.