Zoom factor compensation for monocular SLAM

SLAM algorithms are widely used in augmented reality applications for registering virtual objects. Most SLAM algorithms estimate camera poses and 3D positions of feature points using known intrinsic camera parameters that are calibrated and fixed in advance. This assumption means that the algorithm does not allow changing the intrinsic camera parameters during runtime. We propose a method for handling focal length changes in the SLAM algorithm. Our method is designed as a pre-processing step for the SLAM algorithm input. In our method, the change of the focal length is estimated before the tracking process of the SLAM algorithm. Camera zooming effects in the input camera images are compensated for by using the estimated focal length change. By using our method, camera zooming can be used in the existing SLAM algorithms such as PTAM [4] with minor modifications. In the experiment, the effectiveness of the proposed method was quantitatively evaluated. The results indicate that the method can successfully deal with abrupt changes of the camera focal length.