Efficient Solution of Point-Line Absolute Pose
This work provides a more efficient solution for pose estimation in computer vision, particularly for applications like robotics and augmented reality, though it is incremental as it builds on previously-studied minimal problems.
The paper tackles the problem of camera pose estimation from minimal point-line correspondences by introducing two elementary solutions that reduce the polynomial degrees from 4 to 2 and 8 to 4, respectively, achieving nearly an order of magnitude speedup compared to previous state-of-the-art methods.
We revisit certain problems of pose estimation based on 3D--2D correspondences between features which may be points or lines. Specifically, we address the two previously-studied minimal problems of estimating camera extrinsics from $p \in \{ 1, 2 \}$ point--point correspondences and $l=3-p$ line--line correspondences. To the best of our knowledge, all of the previously-known practical solutions to these problems required computing the roots of degree $\ge 4$ (univariate) polynomials when $p=2$, or degree $\ge 8$ polynomials when $p=1.$ We describe and implement two elementary solutions which reduce the degrees of the needed polynomials from $4$ to $2$ and from $8$ to $4$, respectively. We show experimentally that the resulting solvers are numerically stable and fast: when compared to the previous state-of-the art, we may obtain nearly an order of magnitude speedup. The code is available at \url{https://github.com/petrhruby97/efficient\_absolute}