An Efficient and Accurate Solution to Camera Pose Estimation Problem from Point and Line Correspondences Based on Null Space Analysis

In this paper, we propose an accurate and simultaneously efficient solution to perspective-n-point-and-line (PnPL) problem by null space analysis. Although many PnPL-like methods have been proposed, it is hard to obtain the optimal solution considering both calculation efficiency and accuracy at the same time. Based on the remarkable EOPnP method, the proposed algorithm integrates linear-expressed line constraints with original point constraints, leading to a new solution named EOPnPL. For line error measurement, instead of using the algebraic error built with reprojected endpoints and image lines, we adopt line error function containing the distance from the reprojected midpoint of the model line segment to the image line and giving it a weight 4 times as that of the endpoints. A system of linear homogeneous equations only involving the rotation are derived, containing point and line constraints. Minima are obtained by a null space analysis according to rotation constraints and then the solution is selected by reprojection errors before iterative refinement. Experimental results show that the proposed method showcases great performances in both simulations and real-data experiment on VGG dataset. Great advantages are presented by EOPnPL compared to PnL methods when using only lines. Among the state-of-the-arts methods, the proposed method stands out with high accuracy under ordinary condition and comparable results in planar condition with the speed close to the fastest.