We present a novel smart camera - the FlexSight C1 - designed to enable an industrial robot to detect and localize several types of objects and parts in an accurate and reliable way. The C1 integrates all the sensors and a powerful mini computer with a complete Operating System running robust 3D reconstruction and object localization algorithms on-board, so it can be directly connected to the robot that is guided directly by the device during the production cycle without any external computers in the loop. In this paper, we describe the FlexSight C1 hardware configuration along with the algorithms designed to face the model based localization problem of textureless objects, namely: (1) an improved version of the PatchMatch Stereo matching algorithm for depth estimation; (2) an object detection pipeline based on deep transfer learning with synthetic data. All the presented algorithms have been tested on publicly available datasets, showing effective results and improved runtime performance.

FlexSight - A Flexible and Accurate System for Object Detection and Localization for Industrial Robots / Evangelista, Daniele; Imperoli, Marco; Menegatti, Emanuele; Pretto, Alberto. - (2019), pp. 58-63. (Intervento presentato al convegno IEEE International Workshop on Metrology for Industry 4.0 and IoT tenutosi a Napoli; Italy) [10.1109/METROI4.2019.8792902].

FlexSight - A Flexible and Accurate System for Object Detection and Localization for Industrial Robots

Evangelista, Daniele
Primo
;
Imperoli, Marco
;
Pretto, Alberto
Ultimo
2019

Abstract

We present a novel smart camera - the FlexSight C1 - designed to enable an industrial robot to detect and localize several types of objects and parts in an accurate and reliable way. The C1 integrates all the sensors and a powerful mini computer with a complete Operating System running robust 3D reconstruction and object localization algorithms on-board, so it can be directly connected to the robot that is guided directly by the device during the production cycle without any external computers in the loop. In this paper, we describe the FlexSight C1 hardware configuration along with the algorithms designed to face the model based localization problem of textureless objects, namely: (1) an improved version of the PatchMatch Stereo matching algorithm for depth estimation; (2) an object detection pipeline based on deep transfer learning with synthetic data. All the presented algorithms have been tested on publicly available datasets, showing effective results and improved runtime performance.
2019
IEEE International Workshop on Metrology for Industry 4.0 and IoT
Structured light cameras; Object Detection; Stereo Matching; Deep Learning; Texture-less Objects
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
FlexSight - A Flexible and Accurate System for Object Detection and Localization for Industrial Robots / Evangelista, Daniele; Imperoli, Marco; Menegatti, Emanuele; Pretto, Alberto. - (2019), pp. 58-63. (Intervento presentato al convegno IEEE International Workshop on Metrology for Industry 4.0 and IoT tenutosi a Napoli; Italy) [10.1109/METROI4.2019.8792902].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1305458
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