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.

Boat hunting with semantic segmentation for flexible and autonomous manufacturing

Terreran M.
;
Antonello M.;Ghidoni S.
2019

Abstract

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.
2019
2019 European Conference on Mobile Robots, ECMR 2019 - Proceedings
978-1-7281-3605-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3333357
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