Automatic calibration of camera sensor networks based on 3D texture map information

To construct an intelligent space with a distributed camera sensor network, pre-calibration of all cameras (i.e., determining the absolute poses of each camera) is an essential task that is extremely tedious. This paper considers the automatic calibration method for camera sensor networks based on 3D texture map information of a given environment. In other words, this paper solves a global localization problem for the poses of the camera sensor networks given the 3D texture map information. To manage the complete calibration problem, we propose a novel image descriptor based on quantized line parameters in the Hough space (QLH) to perform a particle filter-based matching process between line features extracted from both a distributed camera image and the 3D texture map information. We evaluate the proposed method in a simulation environment with a virtual camera network and in a real environment with a wireless camera sensor network. The results demonstrate that the proposed system can calibrate complete external camera parameters successfully.