Absolute Pose Estimation from Line Correspondences using Direct Linear Transformation

TL;DR

This paper introduces DLT-Combined-Lines, a novel linear method for camera pose estimation from large sets of 3D/2D line correspondences, achieving high accuracy and efficiency even under noisy conditions.

Contribution

It presents a new combined DLT-based approach that reduces the required line correspondences and improves pose estimation accuracy by exploiting multiple estimates.

Findings

Achieves state-of-the-art orientation accuracy.

Provides the smallest reprojection error under noise.

Estimates pose of 1000 lines in 12 ms.

Abstract

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 matri xestimated 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.