SPL-VINS: superpoint line Vins-Mono

Xiaoyu Tian; Hongyu Cao; Li Li

doi:10.1117/12.3042607

10 September 2024 SPL-VINS: superpoint line Vins-Mono

Xiaoyu Tian, Hongyu Cao, Li Li

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 132570Z (2024) https://doi.org/10.1117/12.3042607
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaoyu Tian, Hongyu Cao, and Li Li "SPL-VINS: superpoint line Vins-Mono", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 132570Z (10 September 2024); https://doi.org/10.1117/12.3042607

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available