Point-line Visual-Inertial Odometry Systems under Short and Long Line Segment Fusion

In visual-inertial odometry (VIO) research, combining point and line features can satisfy high requirements of integrity and restoration for scene reconstruction. In this paper, an integrating strategy of two types of line segments is proposed on the basis of utilizing point and line features for VIO frameworks. First, line segments are classified as short and long categories after extraction and tracking to enhance feature utilization. Then, short lines are spliced and long lines are merged on the basis of distance constraint and consistent direction. In the meanwhile, considering epipolar constraints during back-end processing, triangulation of the two line categories is optimized on the premise of correct matching between adjacent frames, so as to improve accuracy of pose estimation. Finally, point and line feature constraints are utilized simultaneously to reconstruct environments with back-end optimization. Evaluation on the EuRoC dataset shows that localization accuracy and robustness are improved with the proposed strategy. 1