@inproceedings{KediliogluNovaLandesbergeretal.2025, author = {Kedilioglu, Oguz and Nova, Tasnim Tabassum and Landesberger, Martin and Wang, Lijiu and Hofmann, Michael and Franke, J{\"o}rg and Reitelsh{\"o}fer, Sebastian}, title = {PrIcosa: High-Precision 3D Camera Calibration with Non-Overlapping Field of Views}, booktitle = {Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 2)}, editor = {Bashford-Rogers, Thomas and Meneveaux, Daniel and Ammi, Mehdi and Ziat, Mounia and J{\"a}nicke, Stefan and Purchase, Helen and Radeva, Petia and Furnari, Antonino and Bouatouch, Kadi and Sousa, A. Augusto}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-728-3}, doi = {https://doi.org/10.5220/0013088700003912}, pages = {801 -- 809}, year = {2025}, abstract = {Multi-camera systems are being used more and more frequently, from autonomous mobile robots to intelligent visual servoing cells. Determining the pose of the cameras to each other very accurately is essential for many applications. However, choosing the most suitable calibration object geometry and utilizing it as effectively as possible still remains challenging. Disadvantageous geometries provide only subpar datasets, increasing the need for a larger dataset and decreasing the accuracy of the calibration results. Moreover, an unrefined calibration method can lead to worse accuracies even with a good dataset. Here, we introduce a probabilistic method to increase the accuracy of 3D camera calibration. Furthermore, we analyze the effects of the calibration object geometry on the data properties and the resulting calibration accuracy for the geometries cube and icosahedron. The source code for this project is available at GitHub (Nova, 2024).}, language = {en} } @article{LandesbergerKediliogluWangetal.2024, author = {Landesberger, Martin and Kedilioglu, Oguz and Wang, Lijiu and Gan, Weimin and Kornmeier, Joana Rebelo and Reitelsh{\"o}fer, Sebastian and Franke, J{\"o}rg and Hofmann, Michael}, title = {High-Precision Visual Servoing for the Neutron Diffractometer STRESS-SPEC at MLZ}, volume = {24}, pages = {2703}, journal = {Sensors}, number = {9}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s24092703}, year = {2024}, abstract = {With neutron diffraction, the local stress and texture of metallic components can be analyzed non-destructively. For both, highly accurate positioning of the sample is essential, requiring the measurement at the same sample location from different directions. Current sample-positioning systems in neutron diffraction instruments combine XYZ tables and Eulerian cradles to enable the accurate six-degree-of-freedom (6DoF) handling of samples. However, these systems are not flexible enough. The choice of the rotation center and their range of motion are limited. Industrial six-axis robots have the necessary flexibility, but they lack the required absolute accuracy. This paper proposes a visual servoing system consisting of an industrial six-axis robot enhanced with a high-precision multi-camera tracking system. Its goal is to achieve an absolute positioning accuracy of better than 50μm. A digital twin integrates various data sources from the instrument and the sample in order to enable a fully automatic measurement procedure. This system is also highly relevant for other kinds of processes that require the accurate and flexible handling of objects and tools, e.g., robotic surgery or industrial printing on 3D surfaces.}, language = {en} }