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Estimation of intrinsic camera parameters from multiple images of a calibration target. The target recognition and the extraction of the calibration points with subpixel accuracy are carried out automatically.
Use cases: Lens distortion correction, 3D projection
Robust and accurate projector calibration with a complementary gray code sequence. This pattern can be generated for any projector resolution and robustly decoded in the camera image.
Use cases: 3D structured light imaging, augmented reality projection
Compute a rigid transformation between a camera mounted on the robot actuator and the actuator itself by a fixed calibration target in your environment.
Use cases: bin picking
Determine the relative geometric orientation and position of two cameras to each other. Similar to intrinsic calibration, the targets and calibration points are recognized automatically. Intrinsic parameters can be computed during the extrinsic calibration process or input manually.
Use cases: depth sensing, 3D coordinate transformation, sensor fusion
The orientation and position of a camera is determined using a Manhattan world assumption without the use of calibration targets.
Compute geometric transformations between stationary cameras and robot bases as well as robot-tools or -flanges with calibration targets on the robot.
Use cases: robot-assisted inspection control, machine-human collaboration
This introductory tutorial shows how to intrinsically calibrate a camera. You will learn how to specify target detection parameters correctly and how to identify and remove outliers to achieve optimal calibration results.
Learn how to calibrate a projector using a Gray code pattern. The tutorial also shows how to estimate extrinsic parameters of a camera and a projector.
In this tutorial, you will learn how to accurately estimate the rotation and translation of a camera mounted on a gripper with respect to the tool center point.
A tutorial is coming soon ...
A tutorial is coming soon ...
If you are observing a robot with a static camera and want to estimate the position and orientation of the robot base coordinate system in relation to the camera, then follow this tutorial.
Darko Vehar, Rico Nestler, Karl-Heinz Franke: “3D-EasyCalib™ – Toolkit zur geometrischen Kalibrierung von Kameras und Robotern” in 22. Anwendungsbezogener Workshop zur Erfassung, Modellierung, Verarbeitung und Auswertung von 3D-Daten “3D-NordOst”, GFaI Gesellschaft zur Förderung angewandter Informatik e.V. Berlin, S. 15 ff., ISBN: 978-3-942709-24-8, Dezember 2019.