In order to estimate extrinsic parameters for multiple cameras, we use chessboard patterns. However, it is not available when multiple cameras do not have common field of views simultaneously. In this paper, we propose an extrinsic camera calibration ...
In order to estimate extrinsic parameters for multiple cameras, we use chessboard patterns. However, it is not available when multiple cameras do not have common field of views simultaneously. In this paper, we propose an extrinsic camera calibration method using visual features for multiple cameras which do not have common field of views. We adopt SLAM techniques to build a map from individual cameras. Then we apply bundle adjustment to align the maps resulting robust estimation of extrinsic parameters of the cameras. We provide experimental results showing improved performance in terms of reprojection error.