This paper presents a concept which tackles the pose estimation problem (extrinsic calibration) for distributed, non-overlapping multi-camera networks. The basic idea is to use a visual SLAM technique in order to reconstruct the scene from a video which includes areas visible by each camera of the network. The reconstruction consists of a sparse, but highly accurate point cloud, representing a joint 3D reference coordinate system. Additionally, a set of 3D-registered keyframes (images) are used for high resolution representation of the scene which also include a mapping between a set of 2D pixels to 3D points of the point cloud. The pose estimation of each surveillance camera is performed individually by assigning 2D-2D correspondences between pixels of the surveillance camera and pixels of similar keyframes that map to a 3D point. This allows to implicitly obtain a set of 2D-3D correspondences between pixels in the surveillance camera and their corresponding 3D points in a joint reference coordinate system. Thus the global camera pose can be estimated using robust methods for solving the perspective-n-point problem.
['Thomas Pollok', 'Eduardo Monari']
A visual SLAM-based approach for calibration of distributed camera networks