Robust Least-squares Adjustment Based Orientation and Auto-calibration of Wide-baseline Image Sequences

In this paper we propose a strategy for the orientation and auto-calibration of wide-baseline image sequences. Our particular contribution lies in demonstrating, that by means of robust least-squares adjustment in the form of bundle adjustment as well as least-squares matching (LSM), one can obtain highly precise and reliable results. To deal with large image sizes, we make use of image pyramids. We do not need approximate values, neither for orientation nor calibration, because we use direct solutions and robust algorithms, particularly fundamental matrices F, trifocal tensors T , random sample consensus (RANSAC), and auto-calibration based on the image of the dual absolute quadric. We describe our strategy from end to end, and demonstrate its potential by means of examples, showing also one way for evaluation. The latter is based on imaging a cylindrical object (advertisement column), taking the last to be the first image, but without employing the closedness constraint. We finally summarize our findings and point to further directions of research.