Motivation

In the course of setting up a centre for precision measurement and testing technology for optical measurement processes, the focus last year was increasingly on the calibration of cameras and optical sensors. Since the university (i3mainz) has had a mobile industrial robot (MM-800 from Neobotix GmbH consisting of the mobile platform Neobotix MP-800 and industrial robot arm KR16 from Kuka AG) at its disposal since 2016 due to the opening of the laboratory for applied robotics ROBOLAB, which is funded by the Carl Zeiss Foundation, and since many different positions and orientations of the corresponding camera in relation to the calibration field are required during a camera calibration, a robot-supported camera calibration is being considered. The first step will focus exclusively on the calibration of industrial cameras from different manufacturers (e.g. Basler).

Activities

The calibration field is a flat calibration plate with coded and uncoded photogrammetric signals distributed in a grid pattern on the plate. In addition, calibrated scales are included in order to be able to make statements about the external accuracy.

The basis for the automated image acquisition for camera calibration should be an image constellation determined from the sensor/lens constellation, which is approached independently by the industrial robot in the second step. According to the approach position, the image material is to be read out and stored via triggered cameras. The following points are to be implemented for this:

• Control of exposure time, gain and image acquisition from cameras of corresponding manufacturers in a software development (basis for this is MATLAB with various toolboxes).
• Control of the Kuka KR16 robot arm and the mobile platform based on a Python development (Python version 2.7).
• The camera positions form an orbit around the flat calibration field according to the underlying calibration recording constellation. The calculation of the recording positions for the respective orbit is to be implemented in the coordinate system of the calibration field.

• Development of a process chain to determine the transformation parameters between the calibration field coordinate system and the robot coordinate system.

  • This transformation arrangement also includes the determination of the relationship between the robot tool centre point (TCP) and the external orientation of the respective camera.
• Merging the control of camera and robot platform in a corresponding GUI.
• Fully automated image acquisition at predefined positions for the Tool Center Point of the robot arm.
• Semi-automatic image acquisition at predefined positions in a coordinate system superordinate to the robot.

Results

Complete automatic image acquisition at predefined positions in a coordinate system superordinate to the robot is planned for 2019.