Non-Minimal Solvers for Relative Pose Estimation with a Known Relative Rotation Angle

Abstract

Knowing the relative rotation angle improves relative pose estimation accuracy. We consider the problem of computing relative motion from a non-minimal number of correspondences with a known relative rotation angle. While several solvers for minimum correspondences have been proposed, no non-minimal solver for this problem currently exists. In this work, we propose two non-minimal solvers for this problem. The first solver solves the problem using convex relaxation and semidefinite programming, yielding certifiable solutions. The second method approaches the problem through local eigenvalue optimization with random initialization. Increasing the number of initial guesses lowers the chances of missing the correct solution. We conduct experiments on synthetic and real data, confirming our methods' advantages over competing methods.