Authors:
Oguz Kedilioglu
1
;
Tasnim Tabassum Nova
1
;
Martin Landesberger
2
;
Lijiu Wang
3
;
Michael Hofmann
3
;
Jörg Franke
1
and
Sebastian Reitelshöfer
1
Affiliations:
1
Institute for Factory Automation and Production Systems (FAPS), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany
;
2
Technische Hochschule Ingolstadt, Ingolstadt, 85049, Germany
;
3
Heinz Maier-Leibnitz Zentrum (MLZ), Technical University of Munich, Garching, 85748, Germany
Keyword(s):
3D Camera Calibration, Non-Overlapping Field of Views, Icosahedron, Probabilistic.
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).