Recovering camera motion from points and lines in stereo images: A recursive model-less approach using trifocal tensors
Kai Ki Lee, Ying Kin Yu, Kin Hong Wong · 2017
Estimating the 3-D motion of a moving camera from images is a common task in robotics and augmented reality. Most existing marker-less approaches make use of either points or lines. Taking the advantages of both kinds of features in an unknown environment is more attractive due to their availability and differences in characteristics. A novel model-less method is presented in this paper to tackle the 3-D motion tracking problem. Two Bayesian filters, one for point measurements while another for line measurements, are embedded in the Interacting Probabilistic Switching (IPS) framework. They compensate for the weaknesses in one another by utilizing both kinds of features in the stereo images. The proposed method is able to obtain the 3D motion given as little as two line or two point correspondences in consecutive images with the use of multiple trifocal tensors. Our method outperformed two recent methods in terms of accuracy and the problem of drifting was very little in real scenarios.