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This paper proposes a method of extrinsically calibrating cameras in a rectangular room-like environment. It searches for line segments to indicate planar surfaces in the scene, mainly floor, walls and ceiling and uses the homographical relation between matching features on those surfaces to solve for the calibration parameters of the camera and the planes. It is assumed that the environment follows the Manhattan-assumption, i.e. has orthogonal main directions, but we also propose solutions for the general case. We argue that this approach, together with its ability to help describing affine-invariant features to find a larger amount of correct matches, contributes to a larger robustness over epipolar constraint based methods. Due to its cascade-like nature, the method will yield a less accurate estimate, though it serves well as a starting point for bundle adjustment.