SPL-VINS: superpoint line vins mono

Deep learning, with its data-driven advantages, achieves robustness beyond that of traditional algorithms. The integration of deep learning with visual-inertial odometry (VIO) has been a prominent research topic. However, a mature integration solution has yet to emerge. In this paper, we propose SPL-VINS, which combines the deep learning-based feature point detection algorithm SuperPoint with the Vins Mono. Additionally, we add line features into Vins Mono and propose a non-maximum suppression(NMS) method for line features. The residual of line features is modeled in the form of point-to-line distance. Experimental results on the public dataset Euroc demonstrate a significant reduction in absolute translation error and rotation error compared to Vins Mono.