Relative Pose Estimation for Multi-Camera Systems from Point Correspondences with Scale Ratio

Abstract

The use of multi-camera systems is becoming more common in self-driving cars, micro aerial vehicles or augmented reality headsets. In order to perform 3D geometric tasks, the accuracy and efficiency of relative pose estimation algorithms are very important for the multi-camera systems, and is catching significant research attention these days. The point coordinates of point correspondences (PCs) obtained from feature matching strategies have been widely used for relative pose estimation. This paper exploits known scale ratios besides the point coordinates, which are also intrinsically provided by scale invariant feature detectors (e.g., SIFT). Two-view geometry of scale ratio associated with the extracted features is derived for multi-camera systems. Thanks to the constraints provided by the scale ratio across two views, the number of PCs needed for relative pose estimation is reduced from 6 to 3. Requiring fewer PCs makes RANSAC-like randomized robust estimation significantly faster. For different point correspondence layouts, four minimal solvers are proposed for typical two-camera rigs. Extensive experiments demonstrate that our solvers have better accuracy than the state-of-the-art ones and outperform them in terms of processing time.