This work investigates the problem of 6D pose estimation and robot bin picking of non-Lambertian reflecting objects based on a low-cost commercial 3D sensor. In particular, we address the task of estimating the pose of small metal hydraulic components of the same type, randomly placed in a bin. The system consists of a robot arm and an RGB-D sensor in eye-in-hand configuration. The proposed method works in two main phases. In the first phase a Convolutional Neural Network (CNN) extracts the bounding boxes of the objects contained in the bin from a single RGB image of the environment. In the second phase the 6D pose of the objects is estimated using a dense 3D reconstruction of the scene and by applying a template matching algorithm from multiple virtual views of the object CAD model. Experimental results have been carried out on a dataset containing both RGB and depth images. Preliminary experiments are also reported in the real setup.

Detection of Unsorted Metal Components for Robot Bin Picking Using an Inexpensive RGB-D Sensor / Monica, R.; Saccuti, A.; Aleotti, J.; Lippi, M.. - 2022-:(2022), pp. 1-8. (Intervento presentato al convegno 27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022 tenutosi a deu nel 2022) [10.1109/ETFA52439.2022.9921667].

Detection of Unsorted Metal Components for Robot Bin Picking Using an Inexpensive RGB-D Sensor

Lippi M.
2022-01-01

Abstract

This work investigates the problem of 6D pose estimation and robot bin picking of non-Lambertian reflecting objects based on a low-cost commercial 3D sensor. In particular, we address the task of estimating the pose of small metal hydraulic components of the same type, randomly placed in a bin. The system consists of a robot arm and an RGB-D sensor in eye-in-hand configuration. The proposed method works in two main phases. In the first phase a Convolutional Neural Network (CNN) extracts the bounding boxes of the objects contained in the bin from a single RGB image of the environment. In the second phase the 6D pose of the objects is estimated using a dense 3D reconstruction of the scene and by applying a template matching algorithm from multiple virtual views of the object CAD model. Experimental results have been carried out on a dataset containing both RGB and depth images. Preliminary experiments are also reported in the real setup.
2022
27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022
deu
2022
2022-
1
8
Monica, R.; Saccuti, A.; Aleotti, J.; Lippi, M.
Detection of Unsorted Metal Components for Robot Bin Picking Using an Inexpensive RGB-D Sensor / Monica, R.; Saccuti, A.; Aleotti, J.; Lippi, M.. - 2022-:(2022), pp. 1-8. (Intervento presentato al convegno 27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022 tenutosi a deu nel 2022) [10.1109/ETFA52439.2022.9921667].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1308991
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