Boat hunting with semantic segmentation for flexible and autonomous manufacturing
| Autor: | Matteo Terreran, Stefano Ghidoni, Morris Antonello |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science Process (engineering) Scene understanding 02 engineering and technology Semantics Automation Pipeline (software) Semantic segmentation 020901 industrial engineering & automation Human–computer interaction 0202 electrical engineering electronic engineering information engineering Key (cryptography) Robot 020201 artificial intelligence & image processing Segmentation Manufacturing operations Scene understanding Semantic segmentation Autonomous manufacturing business Autonomous manufacturing |
| Zdroj: | ECMR |
| Popis: | Customized mass production of boats and other vehicles requires highly complex manufacturing processes that need a high amount of automation. To enhance the efficiency of such systems, sensing is of paramount importance to provide robots with detailed information about the working environment. In this paper, we propose the use of semantic segmentation to detect the key elements involved in production, to boost automation in the production process. Our main focus is on the sanding process of these tools by means of a robot. We demonstrate the potential of these techniques in an industrial environment featuring a lower degree of variability with respect to the domestic scenes typically considered in the literature. In the production environment, however, higher performances are required to address challenging manufacturing operations successfully. In this work, we also show that exploiting contextual cues and multiple points of view can further boost the reliability of our system, which provides useful data to the other robot modules in charge of navigation, work station recognition, and other tasks. All the methods have been thoroughly validated on the IASLAB RGB-D COROMA Dataset, that was created on purpose. It consists of 46589 RGB-D frames, whose annotation was speeded up thanks to our optimized annotation pipeline. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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