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Bin Picking and Robotic Vision
Múčka, Jan ; Parák, Roman (referee) ; Matoušek, Radomil (advisor)
The aim of this master’s thesis is to describe the Robotic Vision for Bin Picking usage and creating an application for the realization of this task. This application will be able to distinguish several objects based on data from a camera with deep perception and should find the location of object, recognize it and determine its location and orientation. Bin Picking is one of the biggest challenges in today's automation.
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6-DOF Object Localization in Industrial Applications
Macurová, Nela ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
The aim of this work is to design a method for the object localization in the point could and as accurately as possible estimates the 6D pose of known objects in the industrial scene for bin picking. The design of the solution is inspired by the PoseCNN network. The solution also includes a scene simulator that generates artificial data. The simulator is used to generate a training data set containing 2 objects for training a convolutional neural network. The network is tested on annotated real scenes and achieves low success, only 23.8 % and 31.6 % success for estimating translation and rotation for one type of obejct and for another 12.4 % and 21.6 %, while the tolerance for correct estimation is 5 mm and 15°. However, by using the ICP algorithm on the estimated results, the success of the translation estimate is 81.5 % and the rotation is 51.8 % and for the second object 51.9 % and 48.7 %. The benefit of this work is the creation of a generator and testing the functionality of the network on small objects
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Use of "Bin Picking" in industry.
Jirků, Lukáš ; Jirgl, Miroslav (referee) ; Baštán, Ondřej (advisor)
This bachelor thesis aims to familiarize with the technologies of the robotic selection of randomly oriented objects (bin picking) and the subsequent implementation of demonstration tasks. We will also get acquainted with our components (robot, scanner, PLC) and define the requirements for the communication interface between the individual components. Then we describe the demonstration task and design a communication interface. Subsequently, we implement the communication interface and implement the demonstration task.
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Bin Picking for 2D objects
Robota, Jakub ; Parák, Roman (referee) ; Matoušek, Radomil (advisor)
The goal of this bachelor thesis was to process issues of bin picking and to design an aplication for 2D robot vision used for object identification with practical aplication with the use of moving object on axis. Theoretical part of thesis contains research of bin picking and detailed research of methods used in computer vision. Practical part of thesis describes the program which was developed in MATLAB and algorithms, which are used in this program. End of practical part of thesis also describes real use of this program for detecting an object which is moving on axis.
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Identification of 3D objects for Robotic Applications
Hujňák, Jaroslav ; Návrat, Aleš (referee) ; Matoušek, Radomil (advisor)
This thesis focuses on robotic 3D vision for application in Bin Picking. The new method based on Conformal Geometric Algebra (CGA) is proposed and tested for identification of spheres in Pointclouds created with 3D scanner. The speed, precision and scalability of this method is compared to traditional descriptors based method. It is proved that CGA maintains the same precision as the traditional method in much shorter time. The CGA based approach seems promising for the use in the future of robotic 3D vision for identification and localization of spheres.
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Differentiable Depth Estimation for Bin Picking
Černý, Marek ; Klusáček, David (advisor) ; Šikudová, Elena (referee)
The goal of this thesis was to investigate the neural 3D surface reconstruction from multiple views with the intent to use the resulting depth maps for bin picking. Survey of papers from 2014 to 2018 showed that none of the state of the art methods would be used to control a robot arm in our setup. Therefore we decided to create our low-level neural approach which we called the EmfNet. The network is based on a pyramidal resolution refining approach. At each pyramid's layer, there are three separate networks that take part in the computation. Each of them has a definite goal, which gives us almost complete understanding of what is going on inside the network. The EmfNet model was partially usable, but we nevertheless extended it to EmfNet-v2. First, another measuring layer was added, which freed EmfNet from depending on an unnecessary hyperparameter. Second, we used constraints on geometry for the network not to be confused by occlusions (cases where a certain part of the surface is visible only from a single camera). Both networks were implemented and tested on a corpus that was created as a part of this thesis. A corpus containing rendered as well as real data. The process of correspondence pairing inside the network can be observed using the visualization tool. We designed a way how to use a robotic arm...
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6-DOF Object Localization in Industrial Applications
Macurová, Nela ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
The aim of this work is to design a method for the object localization in the point could and as accurately as possible estimates the 6D pose of known objects in the industrial scene for bin picking. The design of the solution is inspired by the PoseCNN network. The solution also includes a scene simulator that generates artificial data. The simulator is used to generate a training data set containing 2 objects for training a convolutional neural network. The network is tested on annotated real scenes and achieves low success, only 23.8 % and 31.6 % success for estimating translation and rotation for one type of obejct and for another 12.4 % and 21.6 %, while the tolerance for correct estimation is 5 mm and 15°. However, by using the ICP algorithm on the estimated results, the success of the translation estimate is 81.5 % and the rotation is 51.8 % and for the second object 51.9 % and 48.7 %. The benefit of this work is the creation of a generator and testing the functionality of the network on small objects
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Identification of 3D objects for Robotic Applications
Hujňák, Jaroslav ; Návrat, Aleš (referee) ; Matoušek, Radomil (advisor)
This thesis focuses on robotic 3D vision for application in Bin Picking. The new method based on Conformal Geometric Algebra (CGA) is proposed and tested for identification of spheres in Pointclouds created with 3D scanner. The speed, precision and scalability of this method is compared to traditional descriptors based method. It is proved that CGA maintains the same precision as the traditional method in much shorter time. The CGA based approach seems promising for the use in the future of robotic 3D vision for identification and localization of spheres.
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Use of "Bin Picking" in industry.
Jirků, Lukáš ; Jirgl, Miroslav (referee) ; Baštán, Ondřej (advisor)
This bachelor thesis aims to familiarize with the technologies of the robotic selection of randomly oriented objects (bin picking) and the subsequent implementation of demonstration tasks. We will also get acquainted with our components (robot, scanner, PLC) and define the requirements for the communication interface between the individual components. Then we describe the demonstration task and design a communication interface. Subsequently, we implement the communication interface and implement the demonstration task.
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|
|
Differentiable Depth Estimation for Bin Picking
Černý, Marek ; Klusáček, David (advisor) ; Šikudová, Elena (referee)
The goal of this thesis was to investigate the neural 3D surface reconstruction from multiple views with the intent to use the resulting depth maps for bin picking. Survey of papers from 2014 to 2018 showed that none of the state of the art methods would be used to control a robot arm in our setup. Therefore we decided to create our low-level neural approach which we called the EmfNet. The network is based on a pyramidal resolution refining approach. At each pyramid's layer, there are three separate networks that take part in the computation. Each of them has a definite goal, which gives us almost complete understanding of what is going on inside the network. The EmfNet model was partially usable, but we nevertheless extended it to EmfNet-v2. First, another measuring layer was added, which freed EmfNet from depending on an unnecessary hyperparameter. Second, we used constraints on geometry for the network not to be confused by occlusions (cases where a certain part of the surface is visible only from a single camera). Both networks were implemented and tested on a corpus that was created as a part of this thesis. A corpus containing rendered as well as real data. The process of correspondence pairing inside the network can be observed using the visualization tool. We designed a way how to use a robotic arm...
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