3D-EasyCalib™ 2.9.14

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Features

Intrinsic Camera Calibration

Intrinsic camera parameters are estimated from multiple images of a calibration target. The target recognition and the extraction of the calibration points with subpixel accuracy are carried out automatically.

Camera-Projector Calibration

We implemented a complementary Gray code sequence to calibrate a projector. This pattern can be generated for any projector resolution and robustly decoded in the camera image.

Hand-Eye

A rigid transformation between a camera mounted on the robot actuator and the actuator itself, i.e. the transformation between the camera and the tool coordinate system is computed. A calibration target is fixed in the environment.

Extrinsic Stereo Calibration

The purpose of stereo calibration is to determine the relative 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 calibration process or input manually.

Manhattan World Calibration

The orientation and position of a camera is determined using the Manhattan world assumption without the use of calibration targets.

Tool-Flange and Robot-World

The Euclidean transformation between the coordinate systems of a stationary camera and a robot base as well as the relative position and orientation of the tool to the flange is determined. A calibration target is fixed to the end effector of the robot.

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Use cases

Intrinsic calibration of a KinectV2 IR camera. On the left side of the window is the list of calibration images. These are shown in color according to the size of the calibration error, from green (min error) to red (max error). In the middle is the image of a selected target pose with detected calibration points (circles) and the points computed according to the camera model (crosses). The color of the circles encodes the absolute difference between these points. The various target poses in the camera’s coordinate system are displayed on the right.
Intrinsic calibration of a KinectV2 IR camera. On the left side of the window is the list of calibration images. These are shown in color according to the size of the calibration error, from green (min error) to red (max error). In the middle is the image of a selected target pose with detected calibration points (circles) and the points computed according to the camera model (crosses). The color of the circles encodes the absolute difference between these points. The various target poses in the camera’s coordinate system are displayed on the right.
Extrinsic calibration of an imaging THz scanner (body scanner) and a monochrome camera. On the left is the image of the target taken with a standard camera and in the middle the image of the same target recorded with the THz camera. The 3D view on the right uses the camera coordinate systems to show the relative orientation and position of these two sensors to another as well as the different target poses.
Extrinsic calibration of an imaging THz scanner (body scanner) and a monochrome camera. On the left is the image of the target taken with a standard camera and in the middle the image of the same target recorded with the THz camera. The 3D view on the right uses the camera coordinate systems to show the relative orientation and position of these two sensors to another as well as the different target poses.
User interface for the robot/world and tool/flange calibration. The left column shows the individual camera recordings with color-coded reprojection errors. In the column next to it, the TCP poses are marked according to the 3D error. A selected image is shown in the center and the components are shown in 3D on the right.
User interface for the robot/world and tool/flange calibration. The left column shows the individual camera recordings with color-coded reprojection errors. In the column next to it, the TCP poses are marked according to the 3D error. A selected image is shown in the center and the components are shown in 3D on the right.
Demonstrator of a workstation for assembly processes at ZBS e. V. (left). The assembly worker’s actions are verified by tracking his hands with several cameras and comparing the information with an assembly process plan. The sensor system consists of a time-of-flight camera for person recognition, three active stereo cameras for component recognition and hand tracking, and a projector for augmentation. The illustration on the right shows the calibrated components of the assembly in the software front end of the 3D-EasyCalib™.
Demonstrator of a workstation for assembly processes at ZBS e. V. (left). The assembly worker’s actions are verified by tracking his hands with several cameras and comparing the information with an assembly process plan. The sensor system consists of a time-of-flight camera for person recognition, three active stereo cameras for component recognition and hand tracking, and a projector for augmentation. The illustration on the right shows the calibrated components of the assembly in the software front end of the 3D-EasyCalib™.
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Steb-by-Step Tutorials

Intrinsic Camera Calibration

This introductory tutorial shows you how to intrinsically calibrate a camera. You will learn how to specify target detection parameters and how to identify and remove outliers.

Camera-Projector Calibration

A camera is used to intrinsically calibrate a projector. You will 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.

Hand-Eye

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.

Extrinsic Stereo Calibration

coming soon ...

  • Sample Files
  • Tutorial

Manhattan World Calibration

coming soon ...

  • Sample Files
  • Tutorial

Tool-Flange and Robot-World

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.

Publications

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.

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