Stereo relative pose from line and point feature triplets
This work addresses a core problem in stereo visual odometry for applications like robotics and autonomous systems, but it is incremental as it builds on existing minimal solver frameworks.
The authors tackled the stereo relative pose problem by developing two minimal solvers for cases with three point or line features, each having three projections on stereo cameras, and demonstrated a considerable effect when integrated into a visual SLAM system.
Stereo relative pose problem lies at the core of stereo visual odometry systems that are used in many applications. In this work, we present two minimal solvers for the stereo relative pose. We specifically consider the case when a minimal set consists of three point or line features and each of them has three known projections on two stereo cameras. We validate the importance of this formulation for practical purposes in our experiments with motion estimation. We then present a complete classification of minimal cases with three point or line correspondences each having three projections, and present two new solvers that can handle all such cases. We demonstrate a considerable effect from the integration of the new solvers into a visual SLAM system.