Publication Details
Absolute Pose Estimation from Line Correspondences using Direct Linear Transformation
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)
Čadík Martin, doc. Ing., Ph.D. (DCGM)
Camera pose estimation, Perspective-n-Line, Line correspondences, Direct linear transformation
This work is concerned with camera pose estimation from correspondences of 3D/2D lines, i. e. with the Perspective-n-Line (PnL) problem. We focus on large line sets, which can be efficiently solved by methods using linear formulation of PnL. We propose a novel method "DLT-Combined-Lines" based on the Direct Linear Transformation (DLT) algorithm, which benefits from a new combination of two existing DLT methods for pose estimation. The method represents 2D structure by lines, and 3D structure by both points and lines. The redundant 3D information reduces the minimum required line correspondences to 5. A cornerstone of the method is a combined projection matrix estimated by the DLT algorithm. It contains multiple estimates of camera rotation and translation, which can be recovered after enforcing constraints of the matrix. Multiplicity of the estimates is exploited to improve the accuracy of the proposed method. For large line sets (10 and more), the method is comparable to the state-of-theart in accuracy of orientation estimation. It achieves state-of-the-art accuracy in estimation of camera position and it yields the smallest reprojection error under strong image noise. The method achieves top-3 results on real world data. The proposed method is also highly computationally effective, estimating the pose of 1000 lines in 12 ms on a desktop computer. The Matlab code of the proposed method and the supplementary material is available for download (http://www.fit.vutbr.cz/~ipribyl/DLT-based-PnL/).
@article{BUT135901,
author="Bronislav {Přibyl} and Pavel {Zemčík} and Martin {Čadík}",
title="Absolute Pose Estimation from Line Correspondences using Direct Linear Transformation",
journal="COMPUTER VISION AND IMAGE UNDERSTANDING",
year="2017",
volume="161",
number="1",
pages="130--144",
doi="10.1016/j.cviu.2017.05.002",
issn="1077-3142",
url="https://authors.elsevier.com/a/1VZpp3qy-3EzCm"
}