Modelling and calibration of multi-camera-systems for 3D industrial supervision applications

With the advent of industry 4.0, the introduction of smart manufacturing and integrated production systems, the interest in 3D image-based supervision methods is growing. The aim of this work is to develop a scalable multi-camera-system suitable for the acquisition of a dense point cloud representing the interior volume of a production machine for general supervision tasks as well as for navigation purposes without a priori information regarding the composition of processing stations. Therefore, multiple low-cost industrial cameras are mounted on the machine housing observing the interior volume. In order to obtain a dense point cloud, this paper reviews aspects of metric stereo calibration and 3D reconstruction with attention being focused on target-based calibration methods and block matching algorithms.