Robust Line Segment Matching for Space-Based Stereo Vision via Multi-Constraint Global Optimization

Robust and accurate line segment matching remains a critical challenge in stereo vision, particularly in space-based applications where weak texture, structural symmetry, and strong illumination variations are common. This paper presents a multi-constraint progressive matching framework that integrates epipolar geometry, coplanarity verification, local homography, angular consistency, and distance-ratio invariance to establish reliable line correspondences. A unified cost matrix is constructed by quantitatively encoding these geometric residuals, enabling comprehensive candidate evaluation. To ensure global consistency and suppress mismatches, the final assignment is optimized using a Hungarian algorithm under one-to-one matching constraints. Extensive experiments on a wide range of stereo image pairs demonstrate that the proposed method consistently outperforms several advanced conventional approaches in terms of accuracy, robustness, and computational efficiency, as evidenced by both quantitative and qualitative evaluations.