Visual-Inertial Odometry (VIO) systems often suffer from degraded performance in environments with low texture. Although some previous works have combined line features with point features to mitigate this problem, the line features still degrade under more challenging conditions, such as varying illumination. To tackle this, we propose DeepLine-VIO, a robust VIO framework that integrates learned line features extracted via an attraction-field-based deep network. These features are geometrically consistent and illumination-invariant, offering improved visual robustness in challenging conditions. Our system tightly couples these learned line features with point observations and inertial data within a sliding-window optimization framework. We further introduce a geometry-aware filtering and parameterization strategy to ensure the reliability of extracted line segments. Extensive experiments on the EuRoC dataset under synthetic illumination perturbations show that DeepLine-VIO consistently outperforms existing point- and line-based methods. On the most challenging sequences under illumination-changing conditions, our approach reduces Absolute Trajectory Error (ATE) by up to 15.87% and improves Relative Pose Error (RPE) in translation by up to 58.45% compared to PL-VINS. These results highlight the robustness and accuracy of DeepLine-VIO in visually degraded environments.
Li et al. (Wed,) studied this question.