WEMA-VINS: Visual-Inertial SLAM With ELSED-MIS Line Filtering

Unmanned aerial vehicles (UAVs) are widely used in various fields due to their flexibility and maneuverability. However, for UAV systems that rely solely on point features, weak-texture environments cause a decline in pose estimation accuracy, creating a severe localization challenge. The fusion of line and point features introduces more geometric constraints, enhancing system robustness and localization accuracy. This letter presents a lightweight monocular visual-inertial SLAM framework, WEMA-VINS (Weak-grid ELSED MIS-filtered and Angular-constrained VINS), designed for robust point-line fusion. It integrates weak-texture region elimination, ELSED line detection, Maximum Independent Set (MIS)-based pruning of redundant segments, and an angular-constrained line optimization module. Our proposed framework collectively improves point feature quality, preserves structural line information, and enhances robustness against endpoint drift and partial occlusion, leading to reliable point-line fusion and efficient state estimation. Extensive experiments on the KAIST VIO and EuRoC datasets demonstrate that WEMA-VINS significantly improves both localization accuracy and robustness, consistently achieving leading performance over recent point-line SLAM baselines while maintaining real-time efficiency.