Thesis Details
6-DOF lokalizace objektů v průmyslových aplikacích
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
6-DOF, pose estimation, point cloud, deep learning, bin picking, PoseCNN, scene simulator, ICP
Čadík Martin, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Češka Milan, prof. RNDr., CSc. (DITS FIT BUT), člen
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT), člen
Chudý Peter, doc. Ing., Ph.D. MBA (DCGM FIT BUT), člen
Szőke Igor, Ing., Ph.D. (DCGM FIT BUT), člen
@mastersthesis{FITMT24163, author = "Nela Macurov\'{a}", type = "Master's thesis", title = "6-DOF lokalizace objekt\r{u} v pr\r{u}myslov\'{y}ch aplikac\'{i}ch", school = "Brno University of Technology, Faculty of Information Technology", year = 2021, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/24163/" }