@inproceedings{d5f233e0bc064e298e762950b8c9f5dc,
title = "SPL-VINS: superpoint line vins mono",
abstract = "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.",
keywords = "Deep learning, feature point detection, line feature, reprojection error, VIO",
author = "Xiaoyu Tian and Hongyu Cao and Li Li",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 2024 International Conference on Advanced Image Processing Technology, AIPT 2024 ; Conference date: 31-05-2024 Through 02-06-2024",
year = "2024",
doi = "10.1117/12.3042607",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Lu Leng and Zhenghao Shi",
booktitle = "International Conference on Advanced Image Processing Technology, AIPT 2024",
address = "United States",
}