Philipp Jatzkowski

Estimation of the absolute camera pose for environment recognition of industrial robotics

The problem of estimating and predicting the absolute camera pose (the position and orientation of a camera with respect to the world coordinate system) is approached by fusion of measurements from inertial sensors (accelerometers and gyroscopes) and robot control system. The sensor fusion approach described in this paper is based on non-linear filtering of multi-rate extended Kalman filter. In this way, camera pose estimates, with improved accuracy and sampling rate as well as reduced computation complexity, are available. Experiments that an industrial robot moves the sensors (camera and inertial measurement unit) in an indoor-global positioning system (GPS)-based global referencing system are presented. The absolute camera pose, provided by indoor-GPS, allows for a performance evaluation. The experimental results confirm also the dynamics improvement of the estimated absolute camera pose.

Cost-Efficient Measurement System Analysis for Small-Batch Production

This paper presents an approach for the selection of methods for the proof of capability of measurement processes according to the risk connected to a false decision. High risks demand more reliable and therefore cost-intensive methods, a low risk allowing for cost-efficient methods with lower reliability. This approach, which is incorporated into the upcoming guideline VDI/VDE 2600-1, allows manufacturers to reduce efforts and costs associated with measurement process evaluation according to the risks and consequences of erroneous decisions deriving from measurement results. To be able to decide for the economically optimal measurement process solution, a cost model for the processes described in the VDI/VDE 2600-1 is presented.