Robust stereo visual-inertial odometry (VIO) remains challenging in low-texture scenes and under abrupt illumination changes, where point features become sparse and unstable, leading to ambiguous association and under-constrained estimation. Line structures offer complementary geometric cues, yet many efficient point-line systems still rely on point-guided line association, which can break down when point support is weak and may lead to biased constraints. We present a stereo point-line VIO system in which line segments are equipped with dedicated deep descriptors and matched using an entropy-regularized optimal transport formulation, enabling globally consistent correspondences under ambiguity, outliers, and partial observations. The proposed descriptor is training-free and is computed by sampling and pooling network feature maps. To improve estimation stability, we analyze the impact of line measurement noise and introduce reliability-adaptive weighting to regulate the influence of line constraints during optimization. Experiments on EuRoC and UMA-VI, together with real-world deployments in low-texture and illumination-challenging environments, demonstrate improved accuracy and robustness over representative baselines while maintaining real-time performance.

翻译：在低纹理场景和光照突变条件下，鲁棒的双目视觉-惯性里程计（VIO）仍然面临挑战，此时点特征变得稀疏且不稳定，导致关联模糊和估计欠约束。线结构提供了互补的几何线索，但许多高效的点-线系统仍依赖于点引导的线关联，当点特征支持不足时该方法可能失效，并可能导致有偏的约束。本文提出了一种双目点-线VIO系统，其中线段配备了专用的深度描述符，并通过熵正则化的最优传输公式进行匹配，从而在模糊、外点和部分观测条件下实现全局一致的对应关系。所提出的描述符无需训练，通过采样和池化网络特征图计算得到。为了提高估计稳定性，我们分析了线测量噪声的影响，并引入了可靠性自适应加权机制，以在优化过程中调节线约束的影响。在EuRoC和UMA-VI数据集上的实验，以及在低纹理和光照挑战环境中的实际部署结果表明，该系统在保持实时性能的同时，相比代表性基线方法实现了更高的精度和鲁棒性。