Tightly-Coupled Visual-Inertial Odometry Using Point and Geometrically Optimized Line Features

Visual-Inertial Odometry (VIO) estimates system pose by fusing visual and inertial measurements. Although line features can enhance structural perception, existing approaches still face challenges such as redundant short segments and weak geometric constraints. To address these, in the front end, we propose a complete geometric optimization pipeline for line features. This pipeline adopts a length-threshold-based filtering strategy and integrates the proposed geometric-consistency-based merging mechanism, endpoint-distance-based verification mechanism, and epipolar-constraint-based triangulation method, transforming fragmented short segments into structurally complete 3D spatial lines. In the back end, reprojection residuals of the optimized line features are jointly optimized with point residuals, IMU pre-integration residuals, and marginalization priors in a sliding-window framework. Experiments on the EuRoC dataset show that compared to VINS-Mono, PL-VINS, and EPLF-VINS, the proposed method reduces the Absolute Pose Error (APE) by 17.57%, 9.88%, and 6.65%, respectively. Additionally, compared to PL-VINS, it reduces the line feature processing time by 4.16% and the average per-frame processing time by 2.36%, validating the effectiveness of the proposed method.